Mixing multiple languages within the same document, a phenomenon called (linguistic) code mixing or code switching, is a frequent trend among multilingual users of social media. In the context of information retrieval (IR), code mixing may affect retrieval effectiveness due to the mixing of different vocabularies with different collection statistics within a single collection of documents. In this paper, we investigate the indexing and retrieval strategies for a mixed collection of documents, comprising of code-mixed and the monolingual documents. In particular, we address three alternative modes of indexing, namely (a) a single index for the two sub-collections; (b) a separate index for each sub-collection; and (c) a clustered index with two individual sub-collection statistics coupled with the overall one. We make use of the expected retrievability scores of the two classes of documents to empirically show that indexing strategies (a) and (b) mostly retrieve the monolingual documents at top ranks with standard retrieval approaches. Our experiments show that, by contrast, the clustered index (c) is able to alleviate this problem by improving the retrievability of the code-mixed documents.