Machine learning has emerged as a versatile tool with potential to accelerate drug discovery. One of the quintessential problems is de novo drug design which involves finding promising candidate molecules that satisfy a multi-parameter optimization (MPO) objective. The major obstacle is the sheer number of possible molecules, estimated to be on the order of 10 , effectively preventing a brute-force search of chemical space. Recently, generative models have been proposed to sample chemical space beyond what is covered by established datasets by conferring the ability to sample novel compounds. Neural network architectures including recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and graph neural networks (GNNs) have demonstrated success in using input data as SMILES or molecular graphs to generate promising chemical ideas. Moreover, reinforcement learning (RL) has been applied in conjunction with generative models to apply an iterative design process in which an agent (a model) learns to generate compounds achieving increasing scores over time. RL encourages the agent to make decisions in order to maximize a reward function which can be tailored to optimize drug-like properties. The synergistic application of generative models and RL has demonstrated potential for de novo drug design by providing a solution to MPO and notably mitigating the computational burden of a brute-force search of chemical space. This approach is also very versatile in terms of components that can be optimized. Notably, quantitative structure-activity relationship (QSAR) models have been applied to a great effect to enrich target activity. However, such models are limited in their generalizability and thus often restricted to relatively small domains of applicability which hinders the ability to sample truly novel compounds. On the other hand, existing physics-based methods such as molecular docking continues to be an invaluable tool to identify molecules that are promising drug candidates. The advent of highperformance computing (HPC) has enabled in silico virtual screening (VS) to consider increasingly larger datasets. This enhanced capability has seen success in identifying more hits with diverse chemotypes and scaffolds, often desirable in structure-based drug discovery (SBDD). VS typically screens molecular libraries whose compounds are readily available, offering the potential to expedite experimentation. However, chemical ideas are inherently restricted to the pre-defined chemical space of these collections which may not satisfy the rigorous criteria in a project. Instead, VS is often used to steer human creativity to a relevant chemical sub-space where iterative design discovers the final drug compound. While strategies such as screening privileged scaffolds can narrow the search space, computational costs of in silico docking rapidly becomes prohibitive and resources may be sub-optimally allocated towards exploring unproductive chemical space. Thus, efficient traversal of chemical space remains a nontrivial endeavour.