A plethora of data sources contain data entities that could be ordered according to a variety of attributes associated with the entities. Such orderings result effectively in a ranking of the entities according to the values in the attribute domain. Commonly, users correlate such sources for query processing purposes through join operations. In query processing, it is desirable to incorporate user preferences towards specific attributes or their values. A way to incorporate such preferences is by utilizing scoring functions that combine user preferences and attribute values and return a numerical score for each tuple in the join result. Then, a target query, which we refer to as top-k join query, seeks to identify the k tuples in the join result with the highest scores. In this paper, we propose a novel technique, which we refer to as ranked join index, to efficiently answer top-k join queries for arbitrary, user specified, preferences and a large class of scoring functions. Our rank join index requires small space (compared to the entire join result) and provides gurantees for its performance. Moreover, our proposal provides a graceful tradeoff between its space requirements and worst case search performance. We supplement our analytical results with a thorough experimental evaluation using a variety of real and synthetic data sets, demonstrating that, in comparison to other viable approaches, our technique offers significantly performance benefits.