In this study, we successfully identified several novel molecules by employing advanced techniques to explore and exploit chemical space. Despite this success, the vast complexity and sheer scale of chemical space present significant challenges. Our models, though generally effective, sometimes struggled with the diversity and complexity found in molecular databases. The accuracy and reliability of our predictions, especially concerning novel or underrepresented molecules, continue to be major concerns. Moreover, scalability is a critical issue; increasing the size and diversity of our datasets tends to exponentially increase computational demands. To overcome these challenges and enhance the accuracy of our exploratory models, it is essential to enrich the diversity of the training datasets. Such improvements will not only boost the models' ability to generalize across various regions of chemical space but will also enhance their predictive accuracy.