diff --git a/docs/src/dist_lsi.rst b/docs/src/dist_lsi.rst
index e80ca3809d..15dfb41f9c 100644
--- a/docs/src/dist_lsi.rst
+++ b/docs/src/dist_lsi.rst
@@ -127,6 +127,7 @@ the corpus iterator with::
     >>> id2word = gensim.corpora.Dictionary.load_from_text('wiki_en_wordids.txt')
     >>> # load corpus iterator
     >>> mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm')
+    >>> # mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm.bz2') # use this if you compressed the TFIDF output
 
     >>> print(mm)
     MmCorpus(3199665 documents, 100000 features, 495547400 non-zero entries)
diff --git a/docs/src/wiki.rst b/docs/src/wiki.rst
index 47aeaa34fd..2992cf8401 100644
--- a/docs/src/wiki.rst
+++ b/docs/src/wiki.rst
@@ -45,6 +45,7 @@ First let's load the corpus iterator and dictionary, created in the second step
     >>> id2word = gensim.corpora.Dictionary.load_from_text('wiki_en_wordids.txt')
     >>> # load corpus iterator
     >>> mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm')
+    >>> # mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm.bz2') # use this if you compressed the TFIDF output (recommended)
 
     >>> print(mm)
     MmCorpus(3931787 documents, 100000 features, 756379027 non-zero entries)
@@ -99,6 +100,7 @@ As with Latent Semantic Analysis above, first load the corpus iterator and dicti
     >>> id2word = gensim.corpora.Dictionary.load_from_text('wiki_en_wordids.txt')
     >>> # load corpus iterator
     >>> mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm')
+    >>> # mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm.bz2') # use this if you compressed the TFIDF output
 
     >>> print(mm)
     MmCorpus(3931787 documents, 100000 features, 756379027 non-zero entries)