To investigate the effect of DF and parameter choice, less computationally expensive scoring functions were required than docking. Therefore, three diverse tasks from the GuacaMol benchmarking suite were chosen and re-implemented according to the original work . The goal the Aripiprazole similarity task is to optimize similarity to Aripiprazole beyond a similarity threshold in order to generate as many similar enough compounds as possible. The goal of the C11H24 isomer task is to generate all 159 molecules with a molecular formula of C11H24, a task involving a more limited pool of molecules. The goal of the Osimertinib MPO task is to optimize similarity to Osimertinib to a certain extent, while ensuring molecules are not too similar and that both lipophilicity and polarity are within a suitable range. The performance of DF parameters was measured by the area under the training curve of three different endpoints: uniqueness (number of unique molecules generated, a proxy of chemical space explored), goal (the score returned by the scoring function/s) and run time (a practical measure to identify if some DFs are slower to compute).