At the root of many clustering algorithms lies the notion of similarity or distance between molecules because such algorithms aim to maximize intra-cluster similarity of member compounds and minimize similarity to other clusters. In order to compute the similarity between compounds, these unsupervised learning techniques most often rely on a vectorized descriptor to represent a molecule, normally a binary fingerprint . Tanimoto and Cosine coefficients are some of the most popular choices for computing similarity between compounds based on binary fingerprints . For example, sequential agglomerative hierarchical non-overlapping (SAHN) algorithms, such as Ward's clustering or unweighted pair group method with arithmetic mean (UPGMA) , start with all compounds as singleton clusters and iteratively merge the most similar ones according to a distance or, alternatively, similarity criterion . The obtained clustering result could be used for diverse and representative subset selection , chemical series identification or structure-activity relationship (SAR) analyses . It is worth noting that these methods are often more computationally expensive relative to rule-based methods, and are also dataset-dependent, meaning that upon changing the dataset, the clustering results might change drastically .