diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index d45b8b33..f2662c15 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -20,7 +20,9 @@ jobs:
       
       - name: Test
         timeout-minutes: 15
-        run: python -m unittest discover -v --start-directory tests/python --pattern "bindings_test*.py"
+        run: |
+          python -m unittest discover -v --start-directory examples --pattern "example*.py"
+          python -m unittest discover -v --start-directory tests/python --pattern "bindings_test*.py"
   
   test_cpp:
     runs-on: ${{matrix.os}}
diff --git a/README.md b/README.md
index 04d84d66..2b027216 100644
--- a/README.md
+++ b/README.md
@@ -80,7 +80,7 @@ For other spaces use the nmslib library https://github.com/nmslib/nmslib.
 * `knn_query(data, k = 1, num_threads = -1, filter = None)` make a batch query for `k` closest elements for each element of the 
     * `data` (shape:`N*dim`). Returns a numpy array of (shape:`N*k`).
     * `num_threads` sets the number of cpu threads to use (-1 means use default).
-    * `filter` filters elements by its labels, returns elements with allowed ids
+    * `filter` filters elements by its labels, returns elements with allowed ids. Note that search with a filter works slow in python in multithreaded mode. It is recommended to set `num_threads=1`
     * Thread-safe with other `knn_query` calls, but not with `add_items`.
     
 * `load_index(path_to_index, max_elements = 0, allow_replace_deleted = False)` loads the index from persistence to the uninitialized index.
diff --git a/examples/EXAMPLES.md b/examples/EXAMPLES.md
index 71f69ff4..a92f3626 100644
--- a/examples/EXAMPLES.md
+++ b/examples/EXAMPLES.md
@@ -147,7 +147,8 @@ print("Querying only even elements")
 # Define filter function that allows only even ids
 filter_function = lambda idx: idx%2 == 0
 # Query the elements for themselves and search only for even elements:
-labels, distances = hnsw_index.knn_query(data, k=1, filter=filter_function)
+# Warning: search with python filter works slow in multithreaded mode, therefore we set num_threads=1
+labels, distances = hnsw_index.knn_query(data, k=1, num_threads=1, filter=filter_function)
 # labels contain only elements with even id
 ```
 
diff --git a/examples/example_filter.py b/examples/example_filter.py
index 10a059a8..add22a3d 100644
--- a/examples/example_filter.py
+++ b/examples/example_filter.py
@@ -41,5 +41,6 @@
 # Define filter function that allows only even ids
 filter_function = lambda idx: idx%2 == 0
 # Query the elements for themselves and search only for even elements:
-labels, distances = hnsw_index.knn_query(data, k=1, filter=filter_function)
+# Warning: search with a filter works slow in python in multithreaded mode, therefore we set num_threads=1
+labels, distances = hnsw_index.knn_query(data, k=1, num_threads=1, filter=filter_function)
 # labels contain only elements with even id
diff --git a/python_bindings/bindings.cpp b/python_bindings/bindings.cpp
index 3196a228..5153bb58 100644
--- a/python_bindings/bindings.cpp
+++ b/python_bindings/bindings.cpp
@@ -623,6 +623,7 @@ class Index {
             data_numpy_l = new hnswlib::labeltype[rows * k];
             data_numpy_d = new dist_t[rows * k];
 
+            // Warning: search with a filter works slow in python in multithreaded mode. For best performance set num_threads=1
             CustomFilterFunctor idFilter(filter);
             CustomFilterFunctor* p_idFilter = filter ? &idFilter : nullptr;
 
diff --git a/tests/python/bindings_test_filter.py b/tests/python/bindings_test_filter.py
index a798e02f..480c8dcd 100644
--- a/tests/python/bindings_test_filter.py
+++ b/tests/python/bindings_test_filter.py
@@ -47,7 +47,8 @@ def testRandomSelf(self):
         print("Querying only even elements")
         # Query the even elements for themselves and measure recall:
         filter_function = lambda id: id%2 == 0
-        labels, distances = hnsw_index.knn_query(data, k=1, filter=filter_function)
+        # Warning: search with a filter works slow in python in multithreaded mode, therefore we set num_threads=1
+        labels, distances = hnsw_index.knn_query(data, k=1, num_threads=1, filter=filter_function)
         self.assertAlmostEqual(np.mean(labels.reshape(-1) == np.arange(len(data))), .5, 3)
         # Verify that there are only even elements:
         self.assertTrue(np.max(np.mod(labels, 2)) == 0)