While the Practical Molecular Optimization (PMO) benchmark is a more recent framework for molecular optimisation, and has a specific focus on function call efficiency, we opted not to use it in our study due to several critical flaws that make it unsuitable for fair comparisons. One major issue is the inconsistency in initial populations: deep generative models are pretrained on the entire initial database, while genetic algorithms in PMO are restricted to a small subset of the first 1000 molecules. This discrepancy skews results and alters the optimisation landscape , making it difficult to draw fair conclusions. Additionally, PMO does not standardise molecules across different tasks, which introduces bias, particularly when charged and neutral molecules are compared by the same pretrained oracle. Finally, PMO includes deep learning based oracles which are easily exploitable and have faced reproducibility issues, as documented in the public code repository .