We see the utility of the proposed model to go beyond what is proposed in the current study. Specifi-cally, we believe that there is potential to extend the discussed setup for other observables in drug discovery that are inherently quantifiable but expensive to obtain experimentally (e.g., compound stability calculations). Additionally, we believe it could provide insights into unexplored regions of chemical space currently ignored when applying simpler mnemonics such as Lipinski's rule of five. With that in mind, we believe that "soft" versions of some popular rule-based filters can be learned by artificially generating training pairs alongside a similar architecture as the one proposed. Such models could potentially overcome the main limitation of having to pre-filter compounds before inference so as to avoid out-of-distribution risks. Another main limitation of the study relates to the simplicity of the question asked during data collection, which was left intentionally vague to capture chemical intuition on a timely manner. Ultimately, whether the proposed function can be successfully used as an aid to experts at any stage in the drug discovery process, and especially in a prospective fashion, remains a topic of further study.