(3) architecture used in this work is a RNN with embedding size 256, three hidden layers of 512 gated recurrent unit (GRU) cells, and a linear layer with softmax activation. Model training followed two steps: first, a prior generative model was trained on the ChEMBL dataset. The agent policy was initialized based on the prior before diverging into individual experiments. The RL was conducted for 1000 epochs, thus iteratively calibrating the agent's likelihood of sampling desirable compounds (see Supporting Information for details).