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In a recent contribution we have not only introduced a complete mathematical framework for extended similarity calculations, (i.e. comparisons of more than two molecules at a time) but defined a series of novel idices. Part 1 is a detailed analysis of the effects of various parameters on the similarity values calculated by the extended formulas. Their features were revealed by sum of ranking differences and ANOVA. Here, in addition to characterizing several important aspects of the newly introduced similarity metrics, we will highlight their applicability and utility in real-life scenarios using datasets with popular molecular fingerprints. Remarkably, for large datasets, the use of extended similarity measures provides an unprecedented speed-up over \u201ctraditional\u201d pairwise similarity matrix calculations. We also provide illustrative examples of a more direct algorithm based on the extended Tanimoto similarity to select diverse compound sets, resulting in much higher levels of diversity than traditional approaches. We discuss the inner and outer consistency of our indices, which are key in practical applications, showing whether the<jats:italic>n<\/jats:italic>-ary and binary indices rank the data in the same way. We demonstrate the use of the new<jats:italic>n<\/jats:italic>-ary similarity metrics on<jats:italic>t<\/jats:italic>-distributed stochastic neighbor embedding (<jats:italic>t<\/jats:italic>-SNE) plots of datasets of varying diversity, or corresponding to ligands of different pharmaceutical targets, which show that our indices provide a better measure of set compactness than standard binary measures. We also present a conceptual example of the applicability of our indices in agglomerative hierarchical algorithms. The Python code for calculating the extended similarity metrics is freely available at:<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/ramirandaq\/MultipleComparisons\">https:\/\/github.com\/ramirandaq\/MultipleComparisons<\/jats:ext-link><\/jats:p>","DOI":"10.1186\/s13321-021-00504-4","type":"journal-article","created":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T12:07:25Z","timestamp":1619179645000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":60,"title":["Extended similarity indices: the benefits of comparing more than two objects simultaneously. 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