Quantification of local similarity between protein 3D structures is a promising tool in computer-aided drug design and prediction of biological function. Over the last ten years, several computational methods were proposed, mostly based on geometrical comparisons. This review summarizes the recent literature and gives an overview of available programs. A particular interest is given to the underlying methodologies. Our analysis points out strengths and weaknesses of the various approaches. If all described methods work relatively well when two binding sites obviously resemble each other, scoring potential solutions remains a difficult issue, especially if the similarity is low. The other challenging question is the protein flexibility, which is indeed difficult to evaluate from a static representation. Last, most of recently developed techniques are fast and can be applied to large amounts of data. Examples were carefully chosen to illustrate the wide applicability domain of the most popular methods: detection of common structural motifs, identification of secondary targets for a drug-like compound, comparison of binding sites across a functional family, comparison of homology models, database screening.