In this work, we employ RF models as property predictors for molecule generation. This choice stems from their demonstrated robustness to small perturbations in high-dimensional feature spaces compared to deep neural networks and linear regression models . Moreover, RF models have been found to be hard to outperform by deep models on a variety of molecular property prediction tasks, thus they are a robust baseline for our problem .