In recent years, there has been increased interest in generative models for molecules in drug discovery ). In the area of de novo molecular design , those models are used to generate molecules with desired properties from scratch. This is often seen as an alternative to virtual screening, which is limited by the size of the libraries that can be searched in practice. Instead of screening existing libraries, generative models can be used to directly create focused libraries for given targets . They can also prove useful in lead optimization, where the aim is to identify a small number of molecules with optimized profiles. Such a profile can encompass a large number of dimensions, e.g. potency, metabolic stability, physicochemical parameters or permeability. This oftentimes arduous process may be accelerated through generative models, which are capable of directly optimizing molecules towards a given profile.