Here, with the aim to improve the sample-efficiency of SMILES-based RNNs, we make a very simple change to the REINVENT strategy to ameliorate overpowered regularization by introducing elements of the HC strategy. We call this novel hybrid approach Augmented Hill-Climb (AHC) and investigate it's use for RNN de novo molecule generation. We further compare AHC to previously mentioned RL strategies that are implemented in published studies and make the code freely accessible .