Despite this limitation, we use this benchmark with caution. In particular, if a large number of carbon atoms is forcefully added to increase the pLogP value, this will be reflected in decreased drug-likeness (QED) and synthetic accessibility (SA) scores. Therefore, we calculate the average changes in the QED and SA scores (calculated using the RDKit package) in addition to the metrics commonly reported in the literature, i.e. success rate and average property improvement. Success rate is measured as the percentage of molecules for which the pLogP has improved (with respect to the starting molecule ∆pLogP>0) while at the same time structural similarity (measured as Tanimoto coefficient on Morgan Fingerprints) is greater or equal than a prespecified cutoff, δ Average improvement of the pLogP value is computed among the molecules that have succeeded. We report the average improvement and success rate at two similarity cutoffs (δ = 0.4 and 0.6) for MOLRL, when paired with a VAE-CYC model and when paired with a MolMIM model, as well as for a set of state-of-the-art molecule optimization algorithms as reported in the literature. We compute the penalized LogP value using the TDC software package (which provides a normalized pLogP value) . We observe that MOLRL in the MolMIM space results in a higher improvement of the average pLogP value while the MOLRL in the VAE space results in a higher success rate with a starker difference at higher structural similarity cutoff δ. These differences in the MOLRL performance in the two latent spaces can be explained by the different degree of latent space continuity between the two models (as shown in Figure ). The MolMIM model has a latent space with a higher degree of continuity which reflects a denser space which gives a bias towards more exploitation than exploration. Conversely, the reduced continuity in the VAE space reflects a more diverse latent space which favors exploration. Higher exploitation can result in a higher property improvement while higher exploration can be beneficial for molecules that cannot be improved with small structural changes. Regarding the method performance in the context of the literature benchmarks, MOLRL on either latent space has achieved success rates on par with the best performing method that is Regression Transformer. Regression Transformer is a language-based model trained on regression tasks using masked language modeling and can additionally be used for conditional molecule generation. This is a stark contrast to the MOLRL method which combines an optimization algorithm with a general-purpose generative model.