Set-valued attributes frequently occur in contexts like market-basked analysis and stock market trends. Late research literature has mainly focused on set containment joins and data mining without considering simple queries on set valued attributes. In this paper we address superset, subset and equality queries and we propose a novel indexing scheme for answering them on set-valued attributes. The proposed index superimposes a trie-tree on top of an inverted file that indexes a relation with set-valued data. We show that we can efficiently answer the aforementioned queries by indexing only a subset of the most frequent of the items that occur in the indexed relation. Finally, we show through extensive experiments that our approach outperforms the state of the art mechanisms and scales gracefully as database size grows.