Although it is possible to optimize RNN de novo molecule generation via fine-tuning on a smaller dataset of molecules relevant to a particular endpoint (as in ), such a priori knowledge is not always available or may bias de novo molecule generation too much towards what is already known resulting in a lack of novelty. Whereas reinforcement learning (RL) can be used to optimize de novo compounds to maximize/minimize a numerical reward which can be provided by either a single or a combination of scoring functions. Several RL strategies have been combined with RNNs including Hill-Climb (HC) , REINFORCE (used in ) and REINVENT . Two of these RL strategies (REINVENT and HC) have been shown to rank top one or two in optimization tasks compared to other generative models . Monte Carlo tree search approaches have also been proposed to search a trained RNN's sample space ; however, no RNN parameters are updated (no RNN learning takes place) during this process and so task optimization is rather an optimized search within the RNNs current generative domain.