diff --git a/test/conftest.py b/test/conftest.py
index b883f81e95..b79b31b380 100644
--- a/test/conftest.py
+++ b/test/conftest.py
@@ -268,7 +268,7 @@ def get_document_store(document_store_type):
             os.remove("haystack_test.db")
         document_store = SQLDocumentStore(url="sqlite:///haystack_test.db")
     elif document_store_type == "memory":
-        document_store = InMemoryDocumentStore(return_embedding=False)
+        document_store = InMemoryDocumentStore(return_embedding=True)
     elif document_store_type == "elasticsearch":
         # make sure we start from a fresh index
         client = Elasticsearch()
@@ -279,7 +279,7 @@ def get_document_store(document_store_type):
             os.remove("haystack_test_faiss.db")
         document_store = FAISSDocumentStore(
             sql_url="sqlite:///haystack_test_faiss.db",
-            return_embedding=False
+            return_embedding=True
         )
         return document_store
     else:
diff --git a/test/test_faiss.py b/test/test_faiss.py
index 45d028818b..752fa0cfff 100644
--- a/test/test_faiss.py
+++ b/test/test_faiss.py
@@ -139,14 +139,15 @@ def test_faiss_finding(document_store, embedding_retriever):
     assert len(prediction.get('answers', [])) == 1
 
 
-def test_faiss_pipeline(faiss_document_store, embedding_retriever):
+@pytest.mark.parametrize("document_store", ["faiss"], indirect=True)
+def test_faiss_pipeline(document_store, embedding_retriever):
     documents = [
         {"name": "name_1", "text": "text_1", "embedding": np.random.rand(768).astype(np.float32)},
         {"name": "name_2", "text": "text_2", "embedding": np.random.rand(768).astype(np.float32)},
         {"name": "name_3", "text": "text_3", "embedding": np.random.rand(768).astype(np.float64)},
         {"name": "name_4", "text": "text_4", "embedding": np.random.rand(768).astype(np.float32)},
     ]
-    faiss_document_store.write_documents(documents)
+    document_store.write_documents(documents)
     pipeline = Pipeline()
     pipeline.add_node(component=embedding_retriever, name="FAISS", inputs=["Query"])
     output = pipeline.run(query="How to test this?", top_k_retriever=3)