Inverted indexes allow to query large databases without needing to search in the database at each query. An important line of research is to construct inverted indexes that require a rather small space usage while still allowing low timings for compression, decompression, and queries. In this article, we show how to use trit encoding, combined with contextual methods for computing inverted indexes. We perform an extensive study of different variants of these methods and show that our method consistently outperforms the Binary Interpolative Method—which is one of the golden standards in this topic—with respect to compression size. We apply our methods to a variety of datasets and make available the source code that produced the results, together with all our datasets.