diff --git a/python/pylibraft/pylibraft/test/test_hnsw.py b/python/pylibraft/pylibraft/test/test_hnsw.py
index 487f190e4e..8cdf8c904f 100644
--- a/python/pylibraft/pylibraft/test/test_hnsw.py
+++ b/python/pylibraft/pylibraft/test/test_hnsw.py
@@ -29,6 +29,7 @@ def run_hnsw_build_search_test(
     k=10,
     dtype=np.float32,
     metric="sqeuclidean",
+    build_algo="ivf_pq",
     intermediate_graph_degree=128,
     graph_degree=64,
     search_params={},
@@ -36,11 +37,18 @@ def run_hnsw_build_search_test(
     dataset = generate_data((n_rows, n_cols), dtype)
     if metric == "inner_product":
         dataset = normalize(dataset, norm="l2", axis=1)
+        if dtype in [np.int8, np.uint8]:
+            pytest.skip(
+                "inner_product metric is not supported for int8/uint8 data"
+            )
+        if build_algo == "nn_descent":
+            pytest.skip("inner_product metric is not supported for nn_descent")
 
     build_params = cagra.IndexParams(
         metric=metric,
         intermediate_graph_degree=intermediate_graph_degree,
         graph_degree=graph_degree,
+        build_algo=build_algo,
     )
 
     index = cagra.build(build_params, dataset)
@@ -57,7 +65,14 @@ def run_hnsw_build_search_test(
     out_dist, out_idx = hnsw.search(search_params, hnsw_index, queries, k)
 
     # Calculate reference values with sklearn
-    nn_skl = NearestNeighbors(n_neighbors=k, algorithm="brute", metric=metric)
+    skl_metric = {
+        "sqeuclidean": "sqeuclidean",
+        "inner_product": "cosine",
+        "euclidean": "euclidean",
+    }[metric]
+    nn_skl = NearestNeighbors(
+        n_neighbors=k, algorithm="brute", metric=skl_metric
+    )
     nn_skl.fit(dataset)
     skl_idx = nn_skl.kneighbors(queries, return_distance=False)
 
@@ -69,9 +84,15 @@ def run_hnsw_build_search_test(
 @pytest.mark.parametrize("k", [10, 20])
 @pytest.mark.parametrize("ef", [30, 40])
 @pytest.mark.parametrize("num_threads", [2, 4])
-def test_hnsw(dtype, k, ef, num_threads):
+@pytest.mark.parametrize("metric", ["sqeuclidean", "inner_product"])
+@pytest.mark.parametrize("build_algo", ["ivf_pq", "nn_descent"])
+def test_hnsw(dtype, k, ef, num_threads, metric, build_algo):
     # Note that inner_product tests use normalized input which we cannot
     # represent in int8, therefore we test only sqeuclidean metric here.
     run_hnsw_build_search_test(
-        dtype=dtype, k=k, search_params={"ef": ef, "num_threads": num_threads}
+        dtype=dtype,
+        k=k,
+        metric=metric,
+        build_algo=build_algo,
+        search_params={"ef": ef, "num_threads": num_threads},
     )