Abstract: Advancements in computational methods have significantly impacted the field of drug discovery by enabling the exploration of complex molecular interactions and the design of diverse chemical libraries. This dissertation presents a study into various computational approaches aimed at enhancing the efficiency and efficacy of early stage drug discovery. Firstly, we investigate changes in the binding pocket of the L99A variant of T4 lysozyme upon binding of a congeneric series of ligands. Using Markov state modeling (MSMs), we characterize the dynamic behavior of protein-ligand interactions and provide insights into the binding mechanisms crucial to rational drug design that need to be addressed in future studies. Secondly, we delve into strategies for building block selection in DNA-encoded library (DEL) design. Leveraging building block-centric approaches, we provide guidelines to construct libraries under specific design constraints and develop predictive models to inform additional computational and experimental follow-up. Collectively, we present computational methods we hope will lead to more efficient and effective strategies for early stage drug design.