A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database. We perform 20 different GA searches using the HTVS scoring function each for four different targets: Bacillus subtilis chorismate mutase (CM), human -adrenergic G protein-coupled receptor (β 2 AR), the DDR1 kinase domain (DDR1), and β -cyclodextrin (BCD). With a population size of 400 this approach generates up to 8000 different potential binders for each target, almost all of which have a better docking score than known binders (Figures and). However, many of these molecules do not resemble drug-like molecules (Figures and) and virtually none of the top-100 scoring molecules are deemed synthetically accessible by the retrosynthetic software package Molecule.one (Table ).