diff --git a/beir/retrieval/search/dense/exact_search.py b/beir/retrieval/search/dense/exact_search.py
index 642b21b..3f36570 100644
--- a/beir/retrieval/search/dense/exact_search.py
+++ b/beir/retrieval/search/dense/exact_search.py
@@ -28,6 +28,7 @@ def search(self,
                top_k: int, 
                score_function: str,
                return_sorted: bool = False, 
+               ignore_identical_ids: bool = True,
                **kwargs) -> Dict[str, Dict[str, float]]:
         # Create embeddings for all queries using model.encode_queries()
         # Runs semantic search against the corpus embeddings
@@ -45,6 +46,9 @@ def search(self,
         logger.info("Sorting Corpus by document length (Longest first)...")
 
         corpus_ids = sorted(corpus, key=lambda k: len(corpus[k].get("title", "") + corpus[k].get("text", "")), reverse=True)
+        if ignore_identical_ids:
+            # We remove the query from results if it exists in corpus
+            corpus_ids = [cid for cid in corpus_ids if cid not in query_ids] 
         corpus = [corpus[cid] for cid in corpus_ids]
 
         logger.info("Encoding Corpus in batches... Warning: This might take a while!")
@@ -70,7 +74,7 @@ def search(self,
             cos_scores[torch.isnan(cos_scores)] = -1
 
             # Get top-k values
-            cos_scores_top_k_values, cos_scores_top_k_idx = torch.topk(cos_scores, min(top_k+1, len(cos_scores[1])), dim=1, largest=True, sorted=return_sorted)
+            cos_scores_top_k_values, cos_scores_top_k_idx = torch.topk(cos_scores, min(top_k, len(cos_scores[1])), dim=1, largest=True, sorted=return_sorted)
             cos_scores_top_k_values = cos_scores_top_k_values.cpu().tolist()
             cos_scores_top_k_idx = cos_scores_top_k_idx.cpu().tolist()
             
@@ -78,13 +82,13 @@ def search(self,
                 query_id = query_ids[query_itr]                  
                 for sub_corpus_id, score in zip(cos_scores_top_k_idx[query_itr], cos_scores_top_k_values[query_itr]):
                     corpus_id = corpus_ids[corpus_start_idx+sub_corpus_id]
-                    if corpus_id != query_id:
-                        if len(result_heaps[query_id]) < top_k:
-                            # Push item on the heap
-                            heapq.heappush(result_heaps[query_id], (score, corpus_id))
-                        else:
-                            # If item is larger than the smallest in the heap, push it on the heap then pop the smallest element
-                            heapq.heappushpop(result_heaps[query_id], (score, corpus_id))
+                    assert ignore_identical_ids is False or corpus_id != query_id, "Query id and corpus id should not be the same if ignore_identical_ids is set to True"
+                    if len(result_heaps[query_id]) < top_k:
+                        # Push item on the heap
+                        heapq.heappush(result_heaps[query_id], (score, corpus_id))
+                    else:
+                        # If item is larger than the smallest in the heap, push it on the heap then pop the smallest element
+                        heapq.heappushpop(result_heaps[query_id], (score, corpus_id))
 
         for qid in result_heaps:
             for score, corpus_id in result_heaps[qid]: