Computational chemists have long desired to correlate protein-ligand complex structures with their binding affinity, especially for designing drugs to combat devastating diseases. However, accurately predicting binding energies has proven challenging. Progress has been slow, with early attempts focusing on ligand studies without considering the protein or the solvent's role. Some advances include qualitative quantitative structure-activity relationship (QSAR) approaches and simplified solvent models. Estimating absolute binding energies remains difficult, but macroscopic and semi-macroscopic models have shown more stability. Evaluating water penetration is problematic due to dynamic water molecule positions. New directions include machine learning and coarse-grained models for protein-protein interactions. Despite current obstacles, computer-aided calculations will eventually revolutionize rational drug design, enhancing automated screening and accelerating drug refinement. The future holds promise for major advances in computer-aided drug design, especially as computer power increases. Open-access collaboration and education of the scientific community about the power of computer-aided drug design will further propel the field forward and enable computers to become the primary tool for developing new medications.