ORGAN JT-VAE GCPN results for a fixed starting point (benzene) and for variable starting points, randomly sampled from ChEMBL. In all tasks, the model consistently optimizes the respective property, reaching relatively high scores already after a few iterations. Although starting from different points, the variance between the scores at a given optimization step is similar to the variance obtained when repeatedly starting from benzene. As a matter of fact, the variance seems to be higher for the fixed starting point. Moreover, starting from a molecule picked from ChEMBL seems to result in faster and more successful optimizations, except for the optimization of the penalized logP score. This is probably due to the increased size and structural complexity of these molecules compared to a simple benzene ring. Moreover, initializing the particle swarm algorithm with more particles helps finding more optimal points in the search space in less iterations (at the expense of increased computational time). Using one GPU for passing the molecules in the swarm through the encoder-decoder model and one CPU core to perform the PSO algorithm and objective function evaluation, computational time is in the order of a few minutes for a 100-steps run. The run time on a 32-core CPU machine without GPU support is in the order of 10 minutes. This is in contrast to baseline methods in Table which have a reported wall clock running time of multiple hours to one day on a 32-core CPU machine .