Optimizing execution of top-k queries over recordid ordered, compressed lists is challenging. The threshold family of algorithms cannot be effectively used in such cases. Yet, improving execution of such queries is of great value. For example, top-k keyword search in information retrieval (IR) engines represents an important scenario where such optimization can be directly beneficial. In this paper, we develop novel algorithms to improve execution of such queries over state of the art techniques. Our main insights are pruning based on fine-granularity bounds and traversing the lists based on judiciously chosen “intervals” rather than individual records. We formally study the optimality characteristics of the proposed algorithms. Our algorithms require minimal changes and can be easily integrated into IR engines. Our experiments on real-life datasets show that our algorithm outperform the state of the art techniques by a factor of 3-6 in terms of query execution times.