With the help of high-throughput experimentation (HTE) for asymmetric catalysis, reproducibility and reliability of data will increase and facilitate the development and integration of predictive computational tools. Automated HTE systems have played a crucial role in accelerating catalyst screening processes, generating vast datasets for diverse reaction conditions. Integrating computational models, as we have described throughout this review, alongside high-throughput screening will not only help overcome limitations related to the number of variables that can be tested (temperature, solvent etc.), but will also contribute to the generalization and robustness of the models developed on datasets gathered under the same experimental conditions. This intersection of automated experimental and computational approaches can then enhance the global efficiency of asymmetric catalysis research, leading to more accurate catalyst design strategies and predictive models.