In order to determine the model architecture we performed a two-stage hyperparameter sweep with weights and biases 20 as described in Table . Briefly, we selected for the best overall model for each of the two active learning selection methods described above, which turned out to be the same architecture for each loss. Each run of the sweep was performed on the train+validation of a single seed of the CCV data split. Our final architecture is shown in Figure . The vast majority of our models were trained on the Morgan fingerprints of the molecule's SMILES with bitsize set to 2048, however we also investigated utilizing a richer input representation to the model. This was done by utilizing the generator of the pre-trained molecule attention transformer (MAT), which is a graph transformer that was pre-trained on ChemBL 22 to predict the properties of masked nodes of the input graph. We utilized the 1024 dimensional vector output of the MAT model generator is a 1024 dimensional vector as input to our model during training.