While molecule generation can also be performed for exploratory purposes (i.e., non goal-oriented), for instance, to diversify chemical libraries for virtual screening , early drug discovery often involves generating molecules with specific properties such as drug-likeness or bioactivity . The key to successful goal-oriented generation is to derive an accurate and representative scoring function that can evaluate molecules for the different desired properties, such that optimizing it results in generating desirable molecules. Once a suitable scoring function is established, the problem of generating desirable molecules can be framed as a discrete optimization problem, which can be effectively tackled using techniques such as reinforcement learning (RL) .