In this paper I present a comparison of graph-based GA and ML results for the optimization of logP values with a constraint for synthetic accessibility and show that GA is as good or better than the ML approaches for this particular property. I also introduce a new non-ML graph-based generative model that can be parameterized using very small data sets and combined with a Monte Carlo tree search algorithm. The implementation of both methods rely on the opens source RDKit cheminformatics package and are made freely available.