The concept of chemical diversity has long been at the forefront of the drug discovery process. From screening to patent busting, access to diverse, original chemical matter is of paramount importance. Chemical diversity is however often challenging to describe objectively. As such, new approaches to quantify, analyze and exploit chemical diversity underpin a wide range of research activity in the fields of cheminformatics, machine learning and computational chemistry. Archetypal molecular similarity metrics, such as the Tanimoto distance between molecular fingerprints, provide a quantitative description of pairwise molecular similarity, which in turn can be used to build up an understanding of mutual chemical diversity within a set of molecules. However, while quantitative, such metrics require an amount of interpretation, and their meaning is not always easily translated into molecular designs. This also extends to diversity-picking algorithms, such as the sphere exclusion algorithm, which can be used to select subsets of molecules each with a predefined minimum distance from each other. While this criterion is satisfied, this can also necessitate a large deal of further investigation to provide contextual understanding.