Databases of molecular structures available to the pharmaceutical industry comprise millions of molecules. With the advent of combinatorial chemistry, a vast number of compounds can be available either physically or virtually, which can make screening all of them infeasible in terms of time and cost. Therefore, only a subset of the entire database that encompasses the full range of structural types of the underlying dataset needs to be selected for screening to maximise the likelihood of finding as many biologically distinct active compounds as possible in a screening experiment. One of most used compound selection method is cluster-based compound selection, which involves subdividing a set of compounds into clusters and choosing one compound or a small number of compounds from each cluster. Selecting only representative compounds from each cluster is based on the assumption that structurally similar molecules have similar properties. A good clustering method groups similar compounds together, to ensure all activity classes are represented, whilst separating active and inactive compounds into different sets of clusters, to avoid an inactive compound being selected as a cluster representative.