diff --git a/haystack/document_stores/memory.py b/haystack/document_stores/memory.py
index 418b1bbeb..bf3029c74 100644
--- a/haystack/document_stores/memory.py
+++ b/haystack/document_stores/memory.py
@@ -15,6 +15,7 @@ import numpy as np
 import torch
 from tqdm.auto import tqdm
 import rank_bm25
+import pandas as pd
 
 from haystack.schema import Document, FilterType, Label
 from haystack.errors import DuplicateDocumentError, DocumentStoreError
@@ -206,7 +207,15 @@ class InMemoryDocumentStore(KeywordDocumentStore):
         index = index or self.index
 
         all_documents = self.get_all_documents(index=index)
-        textual_documents = [doc for doc in all_documents if doc.content_type == "text"]
+        textual_documents = []
+        for doc in all_documents:
+            if doc.content_type == "text":
+                textual_documents.append(doc.content.lower())
+            elif doc.content_type == "table":
+                if isinstance(doc.content, pd.DataFrame):
+                    textual_documents.append(doc.content.astype(str).to_csv(index=False).lower())
+                else:
+                    raise DocumentStoreError("Documents of type 'table' need to have a pd.DataFrame as content field")
         if len(textual_documents) < len(all_documents):
             logger.warning(
                 "Some documents in %s index are non-textual."
@@ -215,7 +224,7 @@ class InMemoryDocumentStore(KeywordDocumentStore):
             )
 
         tokenized_corpus = [
-            self.bm25_tokenization_regex(doc.content.lower())
+            self.bm25_tokenization_regex(doc)
             for doc in tqdm(textual_documents, unit=" docs", desc="Updating BM25 representation...")
         ]
         self.bm25[index] = self.bm25_algorithm(tokenized_corpus, **self.bm25_parameters)
@@ -962,7 +971,7 @@ class InMemoryDocumentStore(KeywordDocumentStore):
         docs_scores = self.bm25[index].get_scores(tokenized_query)
         top_docs_positions = np.argsort(docs_scores)[::-1][:top_k]
 
-        textual_docs_list = [doc for doc in self.indexes[index].values() if doc.content_type == "text"]
+        textual_docs_list = [doc for doc in self.indexes[index].values() if doc.content_type in ["text", "table"]]
         top_docs = []
         for i in top_docs_positions:
             doc = textual_docs_list[i]
diff --git a/test/document_stores/test_memory.py b/test/document_stores/test_memory.py
index ee686f1fa..92d3196f4 100644
--- a/test/document_stores/test_memory.py
+++ b/test/document_stores/test_memory.py
@@ -1,5 +1,6 @@
 import logging
 
+import pandas as pd
 import pytest
 from rank_bm25 import BM25
 
@@ -65,10 +66,19 @@ class TestInMemoryDocumentStore(DocumentStoreBaseTestAbstract):
         assert set(ids) == result
 
     @pytest.mark.integration
-    def test_update_bm25(self, documents):
-        ds = InMemoryDocumentStore(use_bm25=False)
+    def test_update_bm25(self, ds, documents):
         ds.write_documents(documents)
-        ds.update_bm25()
+        bm25_representation = ds.bm25[ds.index]
+        assert isinstance(bm25_representation, BM25)
+        assert bm25_representation.corpus_size == ds.get_document_count()
+
+    @pytest.mark.integration
+    def test_update_bm25_table(self, ds):
+        table_doc = Document(
+            content=pd.DataFrame(columns=["id", "text"], data=[[0, "This is a test"], ["2", "This is another test"]]),
+            content_type="table",
+        )
+        ds.write_documents([table_doc])
         bm25_representation = ds.bm25[ds.index]
         assert isinstance(bm25_representation, BM25)
         assert bm25_representation.corpus_size == ds.get_document_count()