Modern information retrieval systems use sophisticated techniques for efficiency and scalability purposes. Among the most frequent such techniques is the implementation of several levels of caching. The main goal of a cache is to speedup computation by exploiting frequent, recent or costly data used in the past. In this study we propose and evaluate a static cache that works simultaneously as list and intersection cache, offering a more efficient way of handling cache space. In addition, we propose effective strategies to select the term pairs that should populate the cache. Simulation using two datasets and a real query log reveal that the proposed approach improves overall performance in terms of total processing time, achieving savings of up to 40% in the best case.