This paper reviews multiple query approaches for Content-Based Image Retrieval systems (MQIR). These are recently proposed Content-Based Image Retrieval systems that enhance the retrieval performance by conveying a richer understanding of the user high-level interest to the retrieval system. In fact, by allowing the user to express his interest using a set of query images, MQIR bridge the semantic gap with the low-level image features. Nevertheless, the main challenge of MQRI systems is how to compute the distances between the set of query images and each image in the database in a way that enhances the retrieval results and reflects the high-level semantic the user is interested in. For this matter, several approaches have been reported in the literature. In this paper, we investigate existing multiple query retrieval systems. We describe each approach, detail the way it computes the distances between the set of query images and each image in the database, and analyze its advantages and disadvantages in reflecting the high-level semantics meant by the user.