We present a probabilistic framework for learning pair-wise similarities between objects belonging to differ-ent modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modal-ity, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cross-modal constraints, (ii) the prob-abilistic framework naturally allows querying for the most useful/informative constraints, facilitating an ac-tive learning setting (existing methods for cross-modal similarity learning do not have such a mechanism), and (iii) the binary code length is learned from the data. We demonstrate the effectiveness of the proposed approach on two problems that require computing pairwise sim-ilarities between cross-modal object pairs: cross-modal link prediction in bipartite graphs, and hashing based cross-modal similarity search.