The combination of data science with computational chemistry has led to many specialized methods that can greatly speed up material research. In this combined discipline called cheminformatics, scientists develop machine-learning (ML) models for predicting the properties 1 of molecules, folding of macro-molecules, find ways to address combinatorial optimization problems with genetic algorithms and train generative adversarial networks for proposing optimal catalyst surfaces. Typically these methods have in common that they allow handling problems that are too large to be tractable with conventional computational chemistry, while trading for accuracy. One of these cheminformatics techniques is ML-assisted virtual screening. Here, a desired target property (i.e. label value) is predicted for a large number of molecules using descriptors, that are typically easier to obtain than the label value. The candidate molecules in their entirety constitute the so-called chemical space, and are usually chemical derivatives of a core structure.