Datasets of pIC 50 values for tyrosine kinase inhibitor drugs against both KIT and PDGF were procured from BindingDB. The KIT dataset consisted of 1810 unique drug structures, while the PDGF dataset consisted of 2242 structures. On each dataset, a message-passing neural network (MPNN) is trained. We use the Chemprop framework to train the MPNNs. A subset of the hyperparameters of each network (hidden size, depth, dropout and number of feed-forward layers) were tuned using 50 iterations of Bayesian optimization as implemented in the HyperOpt package. The resulting parity plots for each architecture are shown in Fig. . The models achieve Pearson coefficients ≥ 0.88 on the train and test sets (80/10/10 splits), indicating satisfactory performance for our emulator networks. The trained MPNN models are then asked to predict pIC 50 values for the inhibitors in the chemical space defined by Desai et al. . KDE plots in Fig. show the distributions of pIC 50 values for each of the tyrosine kinase receptors across the 270 inhibitors. Importantly, we do not claim that these predictions of inhibitory concentration are accurate with respect to experimental determinations. We are only concerned with constructing a somewhat realistic optimization problem to benchmark our feasibility-aware acquisition functions in a multi-objective setting.