The paper also introduces a new non-ML graph-based generative model (GB-GM) that can be parameterized using very small data sets and combined with a Monte Carlo tree search (MCTS) algorithm such as the one used by Yang et al. . The results are comparable to the results obtained by Yang et al. using a recurrent neural network (RNN) generative model, with maximum J(m)-values of 4.3±0.6 compared to 4.9±0.6 found using 5000 property evaluation. While the results are slightly worse than the RNN results, the GB-GM-based method is orders of magnitude faster. In both cases the MCTS approach essentially extracts the main hydrophopic structural motif (a benzene ring) found in the training set. The MCTS results thus seem more dependent on the composition of the training set than the GA approach for this particular property.