While the results are quite encouraging, it is important to perform similar comparisons for other properties before drawing general conclusions. In a very recent study Brown et al. have compared GB-GA and GB-GM-MCTS to SMILES-based ML and GA methods on 20 different optimization problems and conclude that "In terms of optimization performance, the best model is a genetic algorithm based on a graph representation of molecules." Our and their results strongly suggest that the performance of new ML-based generative models should be compared to more traditional, and often simpler, approaches such as GAs. The both the GB-GA and GB-GM-MCTS codes used in this study are freely available with the open source RDKit toolkit as the only dependence.