For the assessment of HTE-based reaction models, it is important to evaluate the model's performance in a way that reflects real-world applications. While the conventional approach involves using a random split to build a test dataset, recent studies have highlighted that this method can result in overly optimistic performance metrics and may not accurately reflect the challenges faced by chemists in practice. For instance, Doyle and co-workers have argued that random splits allow models to benefit from familiar substrate combinations that might appear in both training and test sets, thereby inflating performance metrics. Similarly, Denmark et al. emphasized that random splitting does not represent real-world scenarios where chemists often encounter novel substrate pairs, leading to a significant drop in model performance when faced with external validation. These findings underscore the importance of more stringent evaluation methods, such as using partially and fully external test sets.