Document retrieval is the task to retrieve from a possibly huge collection of documents those which are most similar to a given query document. In this paper, we present a new heuristic for inexact top K retrieval. It is similar to the well-known index elimination heuristic and is based on Zipf's law, a statistical law observable in natural language texts. We compare the two heuristics with regard to retrieval performance and execution time. Therefore, we use a text collection consisting of scientific articles from various computer science conferences and journals. It turns out that our new approach is not better than index elimination. Interestingly, a combination of both heuristics yields the best results.