Here, we aim to apply GAs to tasks that require the exploration of complex chemical spaces. Virtual Drug Screening can be understood as an optimization problem over a chemical space, i.e., a set of molecules that may be subject to some restriction such as compliance with Lipinski's Rule of Five . The objective function f : X → R estimates e.g. a binding affinity of a candidate x ∈ X to a given drug target. In this setting, very little is known about the properties of the fitness function f , which is either computed from explicit molecular simulations or on the basis of a machine learning model. GAs thus are a very natural choice for the computational methodology to find candidates x ∈ X that at least approximately optimize f (x). Evolutionary algorithms, including GAs, indeed have been used for decades in computer-aided drug discovery (CADD) , see for a survey. Make-on-demand libraries are populated in silico by means of fragment recombination that corresponds to easily accessible chemical reactions. Since the size of such chemical search spaces makes its enumeration impractical and in most cases impossible, GAs are a very natural approach for such problems. A simple example of such an approach is REvold , which is designed to optimize over the ENAMINE REAL Space . In particular in the field for Virtual Drug Screening, it is of interest not only to optimize f but also to access molecules that are sufficiently different from well-known molecular shapes, thus are likely untouched by patents. This reinforces recombination of molecular fragments as an appealing search strategy.