One of the central concerns emphasised in the literature for single-step models pertains to the absence of standardised evaluation metrics essential for enabling meaningful comparisons across various architectures. The current evaluation metric, known as the top-k accuracy, is perceived as misleading . This is because the metric does not test for the ability to propose novel and/or feasible reactions. Instead, it biasedly rewards the effective recall of reactions contained within the database. Additionally, Coley et al. highlights the lack of interpretability within retrosynthesis frameworks. These black-box models generate predictions without offering any underlying reasoning or explanations . This brings forth a critical question: how can chemists place their trust in or even learn from such models when they are not provided with evidence of their reliability and a measure of their interpretability?