Another key aspect of our approach is the use of Tree Edit Distance (TED) from reference routes as a scoring metric. While this method proved effective for our study, it relies on the assumption that reference routes represent optimal synthetic pathways. In practice, this assumption may not always hold -multiple equally valid routes often exist for complex target molecules, and what constitutes an 'optimal' route can depend heavily on context-specific factors like available reagents, equipment constraints, or scaling requirements. Additionally, reference routes drawn from the literature may reflect historical or practical constraints rather than true optimality. Moreover, while our model shows promising performance overall, it encounters some difficulty distinguishing between adjacent categories. Future work could explore more nuanced scoring approaches through human-in-the-loop learning that account for multiple reference routes or incorporate other metrics of synthetic efficiency beyond structural similarity. By incorporating granular expert chemists' feedback in an interactive way for route evaluations, the system could learn to recognize nuances in synthetic strategy that go beyond simple structural similarity to reference routes. potentially leading to a more comprehensive evaluation framework that better reflects the multifaceted nature of route optimization in real-world synthesis planning. This interactive learning approach could help refine both the TED-based scoring and classification boundaries while capturing the tacit knowledge that experienced chemists use to evaluate routes -considerations that may not be apparent from literature references or score scale augmentation alone.