With the advancement of methods and algorithms for predicting catalyst enantioselectivities, computational asymmetric catalysis is a ripe area for further research. In general, the different approaches used to predict these enantioselectivities are broadly distinguished as ones where the mechanism is unknown (statistical models and ML), and ones where TS information for the enantioselective step is (partially) known (QM, MM). Regrettably, the field still requires some understanding of the underlying computational methods and theory before being adopted integrally by experimentalists. Consequently, practical applications of these methods are yet to come. This situation leads most organic chemistry laboratories to continue with employing the conventional, albeit laborious and time-intensive technique of stepwise optimization. As computational methods continue to evolve and their accessibility improves, we envision a future where these tools will be completely integrated in the toolbox of experimentalists, and where the trained models or platforms will be able to improve the discovery rate and unveil new insights in asymmetric catalysis.