Efficiency of machine learning (ML) models is crucial to minimize inference times and reduce carbon footprints of models . This is especially pronounced for ML deployed in production environments, where model queries are more frequent. ML models have become standard in most areas of drug design, including in computer-aided synthesis planning tools. Retrosynthesis analysis predicts the synthesis route from a target molecule to purchasable material. This route is generated using a single-step ML model that predicts reactant molecules for a product molecule. The single-step model is then employed in a multi-step tree search to generate full synthesis routes . Therefore, optimizing single-step retrosynthesis prediction becomes especially relevant as retrosynthesis tools are routinely used by chemists and the computational complexity of the multi-step search is heavily dependent on single-step performance . Single-step retrosynthesis models are either template-based methods, template-free methods, or a combination of both . Transformer encoder-decoder architectures have been used in retrosynthesis research for their ability to scale to large dataset and the promise of extrapolation to novel chemistry. However, no specific architecture has been recognized to outperform, as depending on training data and architectures cover different aspects of the chemical field . Additionally, although single-step accuracy and multi-step solvability are high with template-free methods, inference and search times are too slow to be readily used in production . Furthermore, recent work by Torren-Peraire et al. , Maziarz et al. highlight the difficulty of translating single-step performance to multi-step retrosynthesis success.