Recent advancements in de novo molecular design have positioned generative methods as a complementary approach to traditional virtual screening . Core advantages of these models include the ability to sample chemical space outside the training data and by coupling an optimization algorithm, goal-directed learning can be achieved . Although the field is relatively nascent, molecular generative models have identified experimentally validated therapeutic molecules and organocatalysts . An important shared commonality between these success stories is the inclusion of relatively computationally expensive oracles that are optimized. In drug design, molecular docking is frequently used while in catalyst and materials design, quantum mechanical properties are of interest. Correspondingly, many generative models proposed in recent years have competed to demonstrate accelerated optimization of these properties. However, the heterogeneity of the assessment protocols makes comparisons difficult. Recently, Gao et al. propose the Practical Molecular Optimization (PMO) benchmark which assesses 25 molecular generative models across 23 tasks, enforcing a computational budget of 10,000 oracle calls. Their results show that REINVENT , a recurrent neural network (RNN)-based generative model operating on simplified molecular-input line-entry system (SMILES) is, on average, the most sample efficient generative model. REINVENT uses a policy-based reinforcement learning (RL) algorithm to optimize a reward function in a goal-directed approach. Recently, alternative algorithms have been proposed in the form of Best Agent Reminder (BAR) and Augmented Hill Climbing (AHC) which both introduce bias towards high rewarding molecules to improve sample efficiency. Other studies show that experience replay, where the highest rewarding molecules sampled are stored and replayed to the model, improves sample efficiency. More recently, Bjerrum et al. proposed Double Loop RL to take advantage of the non-injective nature of SMILES and the ease with which they can be augmented. By obtaining different SMILES sequences for the same molecule, oracle scores can be re-used to perform multiple updates to the Agent. Their results show accelerated learning while maintaining the diversity of results, an aspect missing in many proposed benchmarks.