diff --git a/src/Algorithm/branching/branchingalgo.jl b/src/Algorithm/branching/branchingalgo.jl
index 92738b0e6..06281402d 100644
--- a/src/Algorithm/branching/branchingalgo.jl
+++ b/src/Algorithm/branching/branchingalgo.jl
@@ -23,7 +23,7 @@ Divide algorithm that does nothing. It does not generate any child.
 struct NoBranching <: AlgoAPI.AbstractDivideAlgorithm end
 
 function run!(::NoBranching, ::Env, reform::Reformulation, ::Branching.AbstractDivideInput)
-    return DivideOutput([], OptimizationState(getmaster(reform)))
+    return DivideOutput([])
 end
 
 ############################################################################################
@@ -76,11 +76,6 @@ Branching.get_int_tol(ctx::BranchingContext) = ctx.int_tol
 Branching.get_selection_criterion(ctx::BranchingContext) = ctx.selection_criterion
 Branching.get_rules(ctx::BranchingContext) = ctx.rules
 
-function Branching.new_ip_primal_sols_pool(ctx::BranchingContext, reform::Reformulation, input)
-    # Optimization state with no information.
-    return OptimizationState(getmaster(reform))
-end
-
 function _is_integer(sol::PrimalSolution)
     for (varid, val) in sol
         integer_val = abs(val - round(val)) < 1e-5
@@ -130,8 +125,8 @@ function Branching.why_no_candidate(reform::Reformulation, input, extended_sol,
     return _why_no_candidate(master, reform, input, extended_sol, original_sol)
 end
 
-Branching.new_divide_output(children::Vector{SbNode}, optimization_state) = DivideOutput(children, optimization_state)
-Branching.new_divide_output(::Nothing, optimization_state) = DivideOutput(SbNode[], optimization_state)
+Branching.new_divide_output(children::Vector{SbNode}) = DivideOutput(children)
+Branching.new_divide_output(::Nothing) = DivideOutput(SbNode[])
 
 ############################################################################################
 # Branching API implementation for the strong branching
@@ -272,40 +267,24 @@ function new_context(
     )
 end
 
-function Branching.eval_child_of_candidate!(child, phase::Branching.AbstractStrongBrPhaseContext, ip_primal_sols_found, env, reform, input)    
+function Branching.eval_child_of_candidate!(child, phase::Branching.AbstractStrongBrPhaseContext, env, reform, input)    
     child_state = OptimizationState(getmaster(reform))
-    child.optstate = child_state
+    child.conquer_output = child_state
 
-    # In the `ip_primal_sols_found`, we maintain all the primal solutions found during the 
-    # strong branching procedure but also the best primal bound found so far (in the whole optimization).
-    update_ip_primal_bound!(child_state, get_ip_primal_bound(ip_primal_sols_found))
-    
+    global_primal_handler = Branching.get_global_primal_handler(input)
+    update_ip_primal_bound!(child_state, get_global_primal_bound(global_primal_handler))
+        
     if !ip_gap_closed(child_state)
         units_to_restore = Branching.get_units_to_restore_for_conquer(phase)
         restore_from_records!(units_to_restore, child.records)
-        conquer_input = ConquerInputFromSb(Branching.get_global_primal_handler(input), child, units_to_restore)
-        child_state = run!(Branching.get_conquer(phase), env, reform, conquer_input)
-        child.optstate = child_state
+        conquer_input = ConquerInputFromSb(global_primal_handler, child, units_to_restore)
+        child.conquer_output = run!(Branching.get_conquer(phase), env, reform, conquer_input)
         TreeSearch.set_records!(child, create_records(reform))
     end
-    child.conquerwasrun = true
 
     # Store new primal solutions found during the evaluation of the child.
-    add_ip_primal_sols!(ip_primal_sols_found, get_ip_primal_sols(child_state)...)
     for sol in get_ip_primal_sols(child_state)
-        store_ip_primal_sol!(Branching.get_global_primal_handler(input), sol)
+        store_ip_primal_sol!(global_primal_handler, sol)
     end
     return
 end
-
-function Branching.new_ip_primal_sols_pool(ctx::StrongBranchingContext, reform, input)
-    # Optimization state with copy of bounds only (except lp_primal_bound).
-    # Only the ip primal bound is used to avoid inserting integer solutions that are not
-    # better than the incumbent.
-    # We also use the primal bound to init candidate nodes in the strong branching procedure.
-    input_opt_state = Branching.get_conquer_opt_state(input)
-    return OptimizationState(
-        getmaster(reform);
-        ip_primal_bound = get_ip_primal_bound(input_opt_state),
-    )
-end
\ No newline at end of file
diff --git a/src/Algorithm/branching/interface.jl b/src/Algorithm/branching/interface.jl
index 6e2b5fea5..51467f3a1 100644
--- a/src/Algorithm/branching/interface.jl
+++ b/src/Algorithm/branching/interface.jl
@@ -1,10 +1,8 @@
 struct DivideOutput{N} <: Branching.AbstractDivideOutput
     children::Vector{N}
-    optstate::Union{Nothing,OptimizationState}
 end
 
 Branching.get_children(output::DivideOutput) = output.children
-#Branching.get__opt_state(output::DivideOutput) = output.optstate
 
 function get_extended_sol(reform, opt_state)
     return get_best_lp_primal_sol(opt_state)
diff --git a/src/Algorithm/branching/printer.jl b/src/Algorithm/branching/printer.jl
index c53fda230..e36fa9448 100644
--- a/src/Algorithm/branching/printer.jl
+++ b/src/Algorithm/branching/printer.jl
@@ -7,7 +7,6 @@ Branching.get_int_tol(ctx::BranchingPrinter) = Branching.get_int_tol(ctx.inner)
 Branching.get_selection_criterion(ctx::BranchingPrinter) = Branching.get_selection_criterion(ctx.inner)
 Branching.get_selection_nb_candidates(ctx::BranchingPrinter) = Branching.get_selection_nb_candidates(ctx.inner)
 Branching.get_phases(ctx::BranchingPrinter) = Branching.get_phases(ctx.inner)
-Branching.new_ip_primal_sols_pool(ctx::BranchingPrinter, reform, input) = Branching.new_ip_primal_sols_pool(ctx.inner, reform, input)
 
 struct PhasePrinter{PhaseContext<:Branching.AbstractStrongBrPhaseContext} <: Branching.AbstractStrongBrPhaseContext
     inner::PhaseContext
@@ -39,7 +38,7 @@ function Branching.perform_branching_phase!(candidates, cand_children, phase::Ph
         @printf " (lhs=%.4f) : [" Branching.get_lhs(candidate)
         for (node_index, node) in enumerate(children)
             node_index > 1 && print(",")            
-            @printf "%10.4f" getvalue(get_lp_primal_bound(node.optstate))
+            @printf "%10.4f" getvalue(get_lp_primal_bound(node.conquer_output))
         end
         @printf "], score = %10.4f\n" score
     end
diff --git a/src/Algorithm/branching/sbnode.jl b/src/Algorithm/branching/sbnode.jl
index e22d363c5..fb57410d4 100644
--- a/src/Algorithm/branching/sbnode.jl
+++ b/src/Algorithm/branching/sbnode.jl
@@ -11,20 +11,15 @@ mutable struct SbNode <: TreeSearch.AbstractNode
     # There information are printed by the StrongBranchingPrinter.
     # These information will be then transfered to the B&B algorithm when instantating the
     # node of the tree search.
-    optstate::OptimizationState
+    conquer_output::Union{Nothing, OptimizationState}
 
-    var_name::String
     branchdescription::String
+    ip_dual_bound::Bound
     records::Records
-    conquerwasrun::Bool
     function SbNode(
-        reform::Reformulation, depth, var_name::String, branch_description::String, records::Records, input
+        depth, branch_description::String, ip_dual_bound::Bound, records::Records
     )
-        node_state = OptimizationState(
-            getmaster(reform);
-            ip_dual_bound = get_ip_dual_bound(Branching.get_conquer_opt_state(input))
-        )
-        return new(depth, node_state, var_name, branch_description, records, false)
+        return new(depth, nothing, branch_description, ip_dual_bound, records)
     end
 end
 
@@ -32,6 +27,5 @@ getdepth(n::SbNode) = n.depth
 
 TreeSearch.set_records!(n::SbNode, records) = n.records = records
 TreeSearch.get_branch_description(n::SbNode) = n.branchdescription
-get_var_name(n::SbNode) = n.var_name
 TreeSearch.isroot(n::SbNode) = false
 Branching.isroot(n::SbNode) = TreeSearch.isroot(n)
\ No newline at end of file
diff --git a/src/Algorithm/branching/scores.jl b/src/Algorithm/branching/scores.jl
index 6a2a7f0a1..193bcc180 100644
--- a/src/Algorithm/branching/scores.jl
+++ b/src/Algorithm/branching/scores.jl
@@ -4,7 +4,7 @@ function Branching.compute_score(::ProductScore, children, input)
     parent = Branching.get_conquer_opt_state(input)
     parent_lp_dual_bound = get_lp_dual_bound(parent)
     parent_ip_primal_bound = get_ip_primal_bound(parent)
-    children_lp_primal_bounds = get_lp_primal_bound.(getfield.(children, Ref(:optstate)))
+    children_lp_primal_bounds = get_lp_primal_bound.(getfield.(children, Ref(:conquer_output)))
     return _product_score(parent_lp_dual_bound, parent_ip_primal_bound, children_lp_primal_bounds)
 end
 
@@ -14,7 +14,7 @@ function Branching.compute_score(::TreeDepthScore, children, input)
     parent = Branching.get_conquer_opt_state(input)
     parent_lp_dual_bound = get_lp_dual_bound(parent)
     parent_ip_primal_bound = get_ip_primal_bound(parent)
-    children_lp_primal_bounds = get_lp_primal_bound.(getfield.(children, :optstate))
+    children_lp_primal_bounds = get_lp_primal_bound.(getfield.(children, :conquer_output))
     return _tree_depth_score(parent_lp_dual_bound, parent_ip_primal_bound, children_lp_primal_bounds)
 end
 
diff --git a/src/Algorithm/branching/single_var_branching.jl b/src/Algorithm/branching/single_var_branching.jl
index eb4cb0760..584765226 100644
--- a/src/Algorithm/branching/single_var_branching.jl
+++ b/src/Algorithm/branching/single_var_branching.jl
@@ -45,6 +45,8 @@ function Branching.generate_children!(
     units_to_restore = get_branching_candidate_units_usage(candidate, reform)
     d = Branching.get_parent_depth(input)
 
+    parent_ip_dual_bound = get_ip_dual_bound(Branching.get_conquer_opt_state(input))
+
     # adding the first branching constraints
     restore_from_records!(units_to_restore, Branching.parent_records(input))
     setconstr!(
@@ -57,7 +59,7 @@ function Branching.generate_children!(
         members = Dict{VarId,Float64}(candidate.varid => 1.0)
     )
     child1description = candidate.varname * ">=" * string(ceil(lhs))
-    child1 = SbNode(reform, d+1, candidate.varname, child1description, create_records(reform), input)
+    child1 = SbNode(d+1, child1description, parent_ip_dual_bound, create_records(reform))
 
     # adding the second branching constraints
     restore_from_records!(units_to_restore, Branching.parent_records(input))
@@ -71,7 +73,7 @@ function Branching.generate_children!(
         members = Dict{VarId,Float64}(candidate.varid => 1.0)
     )
     child2description = candidate.varname * "<=" * string(floor(lhs))
-    child2 = SbNode(reform, d+1, candidate.varname, child2description, create_records(reform), input)
+    child2 = SbNode(d+1, child2description, parent_ip_dual_bound, create_records(reform))
 
     return [child1, child2]
 end
diff --git a/src/Algorithm/treesearch.jl b/src/Algorithm/treesearch.jl
index 97fde585f..35507241a 100644
--- a/src/Algorithm/treesearch.jl
+++ b/src/Algorithm/treesearch.jl
@@ -166,10 +166,13 @@ function TreeSearch.children(space::AbstractColunaSearchSpace, current::TreeSear
     # Else we run the conquer algorithm.
     # This algorithm has the responsibility to check whether the node is pruned.
     reform = get_reformulation(space)
-    conquer_alg = get_conquer(space)
-    conquer_input = get_input(conquer_alg, space, current)
-    conquer_output = run!(conquer_alg, env, reform, conquer_input)
-    after_conquer!(space, current, conquer_output) # callback to do some operations after the conquer.
+    conquer_output = TreeSearch.get_conquer_output(current)
+    if conquer_output === nothing
+        conquer_alg = get_conquer(space)
+        conquer_input = get_input(conquer_alg, space, current)
+        conquer_output = run!(conquer_alg, env, reform, conquer_input)         
+        after_conquer!(space, current, conquer_output) # callback to do some operations after the conquer.
+    end
     # Build the divide input from the conquer output
     divide_alg = get_divide(space)
     divide_input = get_input(divide_alg, space, current, conquer_output)
diff --git a/src/Algorithm/treesearch/branch_and_bound.jl b/src/Algorithm/treesearch/branch_and_bound.jl
index 9b7ad8497..cbb0bae64 100644
--- a/src/Algorithm/treesearch/branch_and_bound.jl
+++ b/src/Algorithm/treesearch/branch_and_bound.jl
@@ -26,8 +26,6 @@ mutable struct Node <: TreeSearch.AbstractNode
     # Information to restore the reformulation after the creation of the node (e.g. creation
     # of the branching constraint) or its partial evaluation (e.g. strong branching).
     records::Records
-
-    conquerwasrun::Bool # TODO: rename: full_evaluation (?)
 end
 
 getdepth(n::Node) = n.depth
@@ -35,6 +33,7 @@ getdepth(n::Node) = n.depth
 TreeSearch.isroot(n::Node) = n.depth == 0
 Branching.isroot(n::Node) = TreeSearch.isroot(n)
 TreeSearch.set_records!(n::Node, records) = n.records = records
+TreeSearch.get_conquer_output(n::Node) = n.conquer_output
 
 TreeSearch.get_branch_description(n::Node) = n.branchdescription # printer
 
@@ -45,10 +44,9 @@ TreeSearch.get_priority(::TreeSearch.BestDualBoundStrategy, n::Node) = n.ip_dual
 
 # TODO move
 function Node(node::SbNode)
-    ip_dual_bound = get_ip_dual_bound(node.optstate)
     return Node(
-        node.depth, node.branchdescription, node.optstate, ip_dual_bound,
-        node.records, node.conquerwasrun
+        node.depth, node.branchdescription, node.conquer_output, node.ip_dual_bound,
+        node.records
     )
 end
 
@@ -203,7 +201,7 @@ end
 function TreeSearch.new_root(sp::BaBSearchSpace, input)
     nodestate = OptimizationState(getmaster(sp.reformulation), input, false, false)
     return Node(
-        0, "", nothing, get_ip_dual_bound(nodestate), create_records(sp.reformulation), false
+        0, "", nothing, get_ip_dual_bound(nodestate), create_records(sp.reformulation)
     )
 end
 
@@ -215,7 +213,6 @@ function after_conquer!(space::BaBSearchSpace, current, conquer_output)
         store_ip_primal_sol!(space.inc_primal_manager, sol)
     end
     current.records = create_records(space.reformulation)
-    current.conquerwasrun = true
     space.nb_nodes_treated += 1
 
     # Branch & Bound returns the primal LP & the dual solution found at the root node.
diff --git a/src/Branching/Branching.jl b/src/Branching/Branching.jl
index 3ac49c056..14620ff7c 100644
--- a/src/Branching/Branching.jl
+++ b/src/Branching/Branching.jl
@@ -65,15 +65,14 @@ abstract type AbstractDivideContext end
 
 # branching output
 """
-    new_divide_output(children::Union{Vector{N}, Nothing}, ip_primal_sols_found{C, Nothing}) where {N, C} -> AbstractDivideOutput
+    new_divide_output(children::Union{Vector{N}, Nothing}) where {N} -> AbstractDivideOutput
 
 where:
 - `N` is the type of nodes generated by the branching algorithm.
-- `C` is the type of the collection that stores all ip primal solutions found by the branching algorithm.
 
-If no nodes nor ip primal solutions are found, the generic implementation may provide `nothing`.
+If no nodes are found, the generic implementation may provide `nothing`.
 """
-@mustimplement "BranchingOutput" new_divide_output(children, ip_primal_sols_found) = nothing
+@mustimplement "BranchingOutput" new_divide_output(children) = nothing
 
 # Default implementations.
 "Candidates selection for branching algorithms."
@@ -89,7 +88,7 @@ abstract type AbstractBranchingContext <: AbstractDivideContext end
 
 function advanced_select!(ctx::AbstractBranchingContext, candidates, env, reform, input::AbstractDivideInput)
     children = generate_children!(first(candidates), env, reform, input)
-    return new_divide_output(children, nothing)
+    return new_divide_output(children)
 end
 
 ############################################################################################
@@ -124,13 +123,8 @@ strong branching phase.
 "Returns the maximum number of candidates kept at the end of a given strong branching phase."
 @mustimplement "StrongBranching" get_max_nb_candidates(::AbstractStrongBrPhaseContext) = nothing
 
-""
-@mustimplement "StrongBranchingOptState" new_ip_primal_sols_pool(ctx, reform, input) = nothing
-
 function advanced_select!(ctx::Branching.AbstractStrongBrContext, candidates, env, reform, input::Branching.AbstractDivideInput)
     return perform_strong_branching!(ctx, env, reform, input, candidates)
-    #children = get_children(first(candidates))
-    #return new_divide_output(children, ip_primal_sols_found)
 end
 
 function perform_strong_branching!(
@@ -142,10 +136,6 @@ end
 function perform_strong_branching_inner!(
     ctx::AbstractStrongBrContext, env, reform, input::Branching.AbstractDivideInput, candidates::Vector{C}
 ) where {C<:AbstractBranchingCandidate}
-    # We will store all the new ip primal solution found during the strong branching in the
-    # following data structure.
-    ip_primal_sols_found = new_ip_primal_sols_pool(ctx, reform, input)
-
     cand_children = [generate_children!(candidate, env, reform, input) for candidate in candidates]
 
     phases = get_phases(ctx)
@@ -163,7 +153,7 @@ function perform_strong_branching_inner!(
             nb_candidates_for_next_phase = min(nb_candidates_for_next_phase, length(cand_children))
         end
 
-        scores = perform_branching_phase!(candidates, cand_children, current_phase, ip_primal_sols_found, env, reform, input)
+        scores = perform_branching_phase!(candidates, cand_children, current_phase, env, reform, input)
 
         perm = sortperm(scores, rev=true)
         permute!(cand_children, perm)
@@ -178,15 +168,15 @@ function perform_strong_branching_inner!(
         resize!(cand_children, nb_candidates_for_next_phase)
         resize!(candidates, nb_candidates_for_next_phase)
     end
-    return new_divide_output(first(cand_children), ip_primal_sols_found)
+    return new_divide_output(first(cand_children))
 end
 
-function perform_branching_phase!(candidates, cand_children, phase, ip_primal_sols_found, env, reform, input)
-    return perform_branching_phase_inner!(cand_children, phase, ip_primal_sols_found, env, reform, input)
+function perform_branching_phase!(candidates, cand_children, phase, env, reform, input)
+    return perform_branching_phase_inner!(cand_children, phase, env, reform, input)
 end
 
 "Performs a branching phase."
-function perform_branching_phase_inner!(cand_children, phase, ip_primal_sols_found, env, reform, input)
+function perform_branching_phase_inner!(cand_children, phase, env, reform, input)
     
     return map(cand_children) do children
         # TODO; I don't understand why we need to sort the children here.
@@ -205,24 +195,24 @@ function perform_branching_phase_inner!(cand_children, phase, ip_primal_sols_fou
         #     by = child -> get_lp_primal_bound(child)
         # )
 
-        return eval_candidate!(children, phase, ip_primal_sols_found, env, reform, input)
+        return eval_candidate!(children, phase, env, reform, input)
     end
 end
 
-function eval_candidate!(children, phase::AbstractStrongBrPhaseContext, ip_primal_sols_found, env, reform, input)
-    return eval_candidate_inner!(children, phase, ip_primal_sols_found, env, reform, input)
+function eval_candidate!(children, phase::AbstractStrongBrPhaseContext, env, reform, input)
+    return eval_candidate_inner!(children, phase, env, reform, input)
 end
 
 "Evaluates a candidate."
-function eval_candidate_inner!(children, phase::AbstractStrongBrPhaseContext, ip_primal_sols_found, env, reform, input)
+function eval_candidate_inner!(children, phase::AbstractStrongBrPhaseContext, env, reform, input)
     for child in children
-        eval_child_of_candidate!(child, phase, ip_primal_sols_found, env, reform, input)
+        eval_child_of_candidate!(child, phase, env, reform, input)
     end
     return compute_score(get_score(phase), children, input)
 end
 
 "Evaluate children of a candidate."
-@mustimplement "StrongBranching" eval_child_of_candidate!(child, phase, ip_primal_sols_found, env, reform, input) = nothing
+@mustimplement "StrongBranching" eval_child_of_candidate!(child, phase, env, reform, input) = nothing
 
 @mustimplement "Branching" isroot(node) = nothing
 
@@ -302,7 +292,7 @@ function run_branching!(ctx, env, reform, input::Branching.AbstractDivideInput,
     if length(candidates) == 0
         @warn "No candidate generated. No children will be generated. However, the node is not conquered."
         why_no_candidate(reform, input, extended_sol, original_sol)
-        return new_divide_output(nothing, nothing)
+        return new_divide_output(nothing)
     end
     return advanced_select!(ctx, candidates, env, reform, input)
 end
diff --git a/src/TreeSearch/TreeSearch.jl b/src/TreeSearch/TreeSearch.jl
index 607891c22..442a2c6a0 100644
--- a/src/TreeSearch/TreeSearch.jl
+++ b/src/TreeSearch/TreeSearch.jl
@@ -39,6 +39,9 @@ abstract type AbstractNode end
 "Returns the priority of the node depending on the explore strategy."
 @mustimplement "Node" get_priority(::AbstractExploreStrategy, ::AbstractNode) = nothing
 
+"Returns the conquer output if the conquer was already run for this node, otherwise returns nothing"
+get_conquer_output(::AbstractNode) = nothing
+
 ##### Additional methods for the node interface (needed by conquer)
 ## TODO: move outside TreeSearch module.
 @mustimplement "Node" set_records!(::AbstractNode, records) = nothing
diff --git a/test/unit/Branching/branching_default.jl b/test/unit/Branching/branching_default.jl
index a5577abba..a26fd7ced 100644
--- a/test/unit/Branching/branching_default.jl
+++ b/test/unit/Branching/branching_default.jl
@@ -237,7 +237,7 @@ function test_strong_branching()
     # TODO: interface to register Records.
     records = Coluna.Algorithm.Records()
     node = Coluna.Algorithm.Node(
-        0, "", nothing, MathProg.DualBound(reform), records, false 
+        0, "", nothing, MathProg.DualBound(reform), records
     )
 
     global_primal_handler = Coluna.Algorithm.GlobalPrimalBoundHandler(reform)
diff --git a/test/unit/TreeSearch/treesearch.jl b/test/unit/TreeSearch/treesearch.jl
index a875a56de..befca1374 100644
--- a/test/unit/TreeSearch/treesearch.jl
+++ b/test/unit/TreeSearch/treesearch.jl
@@ -133,12 +133,12 @@ function Coluna.Algorithm.run!(alg::DeterministicDivide, ::Coluna.Env, ::Coluna.
     for c in children                             ## update flag
         push!(alg.nodes_created_by_divide, c.id)
     end
-    return Coluna.Algorithm.DivideOutput(children, nothing) 
+    return Coluna.Algorithm.DivideOutput(children) 
 end
 
 # constructs a real node from a LightNode, used in new_children to built real children from the minimal information contained in LightNode
 function Coluna.Algorithm.Node(node::LightNode)
-    return Coluna.Algorithm.Node(node.depth, " ", nothing, node.parent_ip_dual_bound, Coluna.Algorithm.Records(), false)
+    return Coluna.Algorithm.Node(node.depth, " ", nothing, node.parent_ip_dual_bound, Coluna.Algorithm.Records())
 end
 
 ## The candidates are passed as LightNodes and the current node is passed as a TestBaBNode. The method retrieves the inner nodes to run the native method new_children of branch_and_bound.jl, gets the result as a vector of Nodes and then re-built a solution as a vector of TestBaBNodes using the nodes ids contained in LightNode structures.