The past decades have witnessed continuous incremental improvements in the computational methods used to facilitate small molecule drug discovery and development. However, the field is currently witnessing a revived optimism, fueled by continuous leaps in computational power, increased accessibility to diverse chemical space, improved physics-based methods, and the emerging potential of newer machine learning and AI approaches. It is fair to say that the question is not whether in silico design will grow as a part of drug discovery, but how much and how quickly these methods will reduce the experimental discovery cycle. This optimism must be tempered by the fact that considerable additional progress is needed. Today, no algorithm can select, design, or rank potent, drug-like small molecule protein binders consistently. Predicting pharmacokinetic, pharmacodynamic and toxicity (ADMET) properties is even more difficult, as this involves the interactions of a candidate drug with the entire biological system, rather than a single protein target.