Figure examines the prior NLL distribution of unique compounds generated by RL DF(scaffold) for compound 1 with varying learning rates. With a higher learning rate (up to 1e-4), a larger chemical space, deviating from prior, is explored. This is because RL helps steering the agent towards the chemical space with favourable properties, potentially directing it away from prior. Consequently, the agent has a higher chance (lower NLL) to generate molecules with desirable properties than the prior (Figure ).