AHC was then implemented in an effort to improve sample-efficiency, while also varying σ for over the same amount of RL updates (Figure ). This consistently led to improved optimization ability for every σ value compared to REINVENT, with a maximum of 205% optimization with σ=240. In total, we found a 1.39-fold improvement in optimization ability compared to REINVENT averaged across all values of σ. Moreover, AHC required approximately 80 fewer steps to achieve the mean docking score achieved by REINVENT over 100 steps, evidencing a large improvement in sample-efficiency. However, learning was stifled by a drop in uniqueness observed (Figure ) i.e., AHC was more prone to mode collapse.