This paper reports the application of techniques inspired by text retrieval research to content-based image retrieval. In particular, we show how the use of an inverted file data structure permits the use of an extremely high-dimensional feature-space, by restricting search to the subspace spanned by the features present in the query. A suitably sparse set of colour and texture features is proposed. A weighting scheme based on feature frequencies is used to combine disparate features in a compatible manner, and naturally extends to incorporate relevance feedback queries. The use of relevance feedback is shown consistently to improve system performance.