diff --git a/haystack/document_store/memory.py b/haystack/document_store/memory.py
index 9814e7888..1ebbf9645 100644
--- a/haystack/document_store/memory.py
+++ b/haystack/document_store/memory.py
@@ -107,11 +107,6 @@ class InMemoryDocumentStore(BaseDocumentStore):
         from numpy import dot
         from numpy.linalg import norm
 
-        if filters:
-            raise NotImplementedError("Setting `filters` is currently not supported in "
-                                      "InMemoryDocumentStore.query_by_embedding(). Please remove filters or "
-                                      "use a different DocumentStore (e.g. ElasticsearchDocumentStore).")
-
         index = index or self.index
         if return_embedding is None:
             return_embedding = self.return_embedding
@@ -119,8 +114,9 @@ class InMemoryDocumentStore(BaseDocumentStore):
         if query_emb is None:
             return []
 
+        document_to_search = self.get_all_documents(index=index, filters=filters, return_embedding=True)
         candidate_docs = []
-        for idx, doc in self.indexes[index].items():
+        for doc in document_to_search:
             curr_meta = deepcopy(doc.meta)
             new_document = Document(
                 id=doc.id,
@@ -185,7 +181,7 @@ class InMemoryDocumentStore(BaseDocumentStore):
         """
         index = index or self.label_index
         return len(self.indexes[index].items())
-      
+
     def get_all_documents(
             self,
             index: Optional[str] = None,
diff --git a/test/test_dpr_retriever.py b/test/test_dpr_retriever.py
index 685ef191b..9433ed4dc 100644
--- a/test/test_dpr_retriever.py
+++ b/test/test_dpr_retriever.py
@@ -3,6 +3,7 @@ import time
 import numpy as np
 
 from haystack import Document
+from haystack.document_store.faiss import FAISSDocumentStore
 from haystack.retriever.dense import DensePassageRetriever
 
 from transformers import DPRContextEncoderTokenizerFast, DPRQuestionEncoderTokenizerFast
@@ -62,6 +63,13 @@ def test_dpr_retrieval(document_store, retriever, return_embedding):
     else:
         assert res[0].embedding is None
 
+    # test filtering
+    if not isinstance(document_store, FAISSDocumentStore):
+        res = retriever.retrieve(query="Which philosopher attacked Schopenhauer?", filters={"name": ["0", "2"]})
+        assert len(res) == 2
+        for r in res:
+            assert r.meta["name"] in ["0", "2"]
+
 
 @pytest.mark.parametrize("retriever", ["dpr"], indirect=True)
 @pytest.mark.parametrize("document_store", ["memory"], indirect=True)