Regarding the average property improvement, MOLRL in the MolMIM space has comparable performance with the best performing methods without sacrificing desirable molecular properties as shown in Table . Table shows the average changes in the QED and SA scores among the succeeded molecules for MOLRL and MolDQN as a percentage change with respect to the starting molecules scores. As a baseline here we chose an approach that applies RL directly on the molecular structure as opposed to MOLRL which modifies molecules through their latent representation. (MolDQN was chosen here for the additional reason of being the only method we were able to reproduce reported results). We observe that MOLRL compromises QED to improve pLogP however at a significantly smaller extent than MolDQN. On the other side, the SA score is being improved (lower SA score indicates easier synthesis) with a bigger change for the model operating in the MolMIM space. MolDQN though compromises both scores, QED and SA, to achieve higher pLogP values. The smaller effect on QED and the improvement of SA that are observed for MOLRL are a result of working in the latent space of a model that has been trained on a large chemical dataset and therefore has captured the distribution of the properties of the molecules it has been trained on. In the literature, QED and SA changes have not been reported for this benchmark. However, it has been observed that existing approaches tested on the pLogP benchmark often learn a simple policy of just adding carbon chains .