Whilst one can select a variety of evaluation metrics to assess the performance of a retrosynthesis framework , the top-k accuracy is by far the most popular. First introduced by Liu et al. , it was used to compare the novel SMILES model to the expert-base system. In short, the top-k accuracy calculates the percentage of instances for which the model's top-k predictions include the actual ground-truth reactants from the test database. Here, the term "ground-truth" pertains to the set of reactants documented in the dataset. While it remains crucial for the model to acquire knowledge from the dataset and faithfully reproduce the existing data, it is equally significant for the model to generate innovative chemical reactions that can be tested experimentally. Since the top-k accuracy solely focuses on whether the ground-truth (1 out of k reactions) is found among the top predictions, it inherently fails to gauge the model's capacity to propose a diverse and feasible set of potential synthesis routes, rendering this metric flawed in that aspect.