In response to this obstacle, several approaches have been employed. The most common method involves using a computational metric known as Synthetic Accessibility (SA) as a proxy for compound availability and pricing during virtual screenings. SA scores provide an assessment of how complex it is to synthesize a compound . However, empirical investigations have revealed that SA scores do not directly correlate with the actual prices of compounds . As an alternative solution, Machine Learning (ML) models have emerged by predicting compound prices based on their underlying chemical structures . While these models offer more consistent results compared to SA scores, they exhibit inherent limitations, such as constrained predictive efficacy outside their applicability domains . Furthermore, it is imperative to acknowledge the dynamic nature of compound prices, subject to temporal fluctuations, necessitating recurrent model retraining . Consequently, compound pricing is a complex matter influenced by various factors, including supply and demand dynamics, production intricacies, storage and transportation costs, patent considerations, competitive market forces, and government policies . This complexity means that accurate prediction of compound prices solely from the inherent characteristics of the compounds is not feasible.