High-throughput experimentation (HTE) has emerged as a powerful alternative to traditional literature-based approaches for building reaction yield prediction models. HTE techniques generate large datasets through automated, parallelized experiments, offering a more consistent, comprehensive, and controlled data source with a broader range of reaction conditions, including low-yield and negative outcomes. This systematic approach helps in developing more robust and generalizable models. With our in-house automated HTE platform, we have successfully optimized reaction conditions, explored the reaction space, and collected standardized experimental data for machine learning studies, resulting in a series of related publications. Despite these advantages, many HTE-based models achieve high accuracy but are limited to a narrow range of substrate and reaction condition spaces. This limitation has been well documented by several research groups, including those led by Doyle, Sigman, and Denmark. Additionally, a common issue with these models is the evaluation methodology.