Hill-Climb (HC) is an alternative policy-based strategy benchmarked in that shows state-of-the-art or near state-of-the-art performance. HC can also be interpreted as a form of iterative fine-tuning (where fine-tuning molecules are selected by the scoring function rather than e.g., known activity against a certain target). The agent RNN first samples a batch of molecules, and then the RNN is fine-tuned using the same loss function as Equation 1 but using only the top k molecules from the batch, as ranked according to some reward assigned to each molecule. This algorithm is depicted in the top part of Figure .