Network science has proven to be extremely successful in understanding of complex systems. In recent years, the study of systems comprised of numerous types of relations i.e. multiplex networks has brought higher resolution details on dynamics of these systems. Link prediction puts networks under the microscope from the angle of associations among node pairs. Although link prediction in single-layer networks has a long history, efforts on the same task in multiplex networks are not plentiful. In this study, question under discussion is that, how trans-layer correlations in a multiplex network can be used to enhance prediction of missing links. It is shown that in a wide-range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Subsequently, a similarity-based automatic general-purpose multiplex link prediction method -- SimBins -- is devised that for an arbitrary layer of a multiplex network, employs the structural features from both the layer itself and an additional auxiliary layer via information theoretic techniques. Applied to various datasets from different contexts, SimBins proves to be robust and superior than compared methods in majority of experimented cases in terms of accuracy of link prediction. Furthermore, it is discussed that SimBins imposes minor computation overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.