Computational methods for catalyst design and discovery. As an analogy, over the past decades, dockingbased virtual screening has found extensive use and acceptance as a design tool in medicinal chemistry. The low computational demands of these methods and user-friendly interfaces removed hurdles towards their widespread adoption. In contrast, computational tools that could improve the process of chemical reaction development remain underutilized as predictive/design methods. The power of quantum mechanics (QM) calculations, particularly density functional theory (DFT), is primarily used in a retrospective, post-hoc fashion for understanding reaction mechanisms and for rationalizing observed selectivities, rather than in the prediction/design of new catalysts. In fact, the computational cost associated with ab initio QM or DFT methods, let alone the required expert knowledge, makes them unsuitable for the screening of large libraries of potential catalysts. However, major efforts are currently ongoing to develop computational tools to assist organic chemists, and integration into organic chemistry laboratories is imminent. We believe that the successes of virtual screening in medicinal chemistry should be adaptable to reaction prediction, with the challenge being the availability of computationally inexpensive, rapid, and accurate methods for predicting stereoselectivities associated with complex TS structures and energies.