There is an urgent need for drugs which target SARS-CoV-2, the pathogen responsible for the current coronavirus pandemic. In this regard, a concerted global effort has led to a rapid rise in the number of SARS-CoV-2 protein structures available in the Protein Data Bank (PDB), rendering the virus increasingly susceptible to rational, structure-based drug discovery. The typical timeline for the development of a single drug is 10-15 years, with an associated cost of $2 billion. In the face of the global COVID-19 pandemic, it is clear that the average development timescale of up to 15 years is wholly inadequate. It is therefore of crucial humanitarian and societal importance to develop new in silico workflows that accelerate the rate and enhance the quality of lead drug molecule design. Workflows which tie both artificial intelligence (AI) and molecular dynamics (MD) based methods together are required as no single methodology can achieve both the required accuracy and speed. Whilst AI based methods can rapidly sample significant regions of chemical space in a short time frame, MD based methods (which are significantly lower in throughput) are able to predict ligand binding free energies to much higher accuracy. Furthermore, MD based methods have the potential to elucidate ligand binding kinetics and processes. The information derived from these simulations can be used to inform drug molecule optimisation for improved kinetic and thermodynamic binding properties. In turn, MD based methods form a crucial part of modern drug discovery workflows. In the present work, we investigate the application of molecular dynamics (MD) simulations to the robust and reproducible elucidation of ligand binding mechanisms, sites and interactions.