All inexpensive scoring components (in this case QED, molecular weight, hydrogen bond donors and substructure alerts) are computed for all compounds. Then, an acquisition function is used to select Construction of appropriate predictive models for molecules is richly studied , but not the focus of the current work. Based on retrospective testing on a standard RL run, we evaluated several classical (RF, support vector regression, gradient boosting, k-nearest neighbors, Gaussian Process regression) and deep learning approaches (ChemProp) and found limited impact of model choice on surrogate model accuracy (Supporting Figure ). Additionally, we observed limited impact for featurization method (Supporting Figure ). We use RF with