scikit-fingerprints (https://github.com/scikit-fingerprints/scikit-fingerprints) is a scikit-learn compatible library for computing molecular fingerprints, molecular filters, distance measures, applicability domain algorithms, and more, on top of RDKit. It accelerates machine learning (ML) workflows in chemoinformatics by integrating those two software ecosystems, offering a unified, Pythonic interface over RDKit functionalities. It is the most mature project of this type, featuring the widest functionality range, distribution with PyPI, and a comprehensive documentation. Furthermore, it has been applied to multiple projects, including e.g. peptide property prediction, agrochemistry, and integration into BayBE experiment design framework.