We have developed and implemented a baseline retrosynthetic tool with only a single neural network, to investigate the role of the ML template prioritization method in the tree search algorithm derived from the work of Segler and Waller. We have found that models trained on datasets as small as the internal ELN (4.8 % of all templates) and USPTO datasets (44.8 % of all templates), are sufficient for the prediction of synthetic routes to compounds found in medicinal chemistry pipelines. Furthermore, we demonstrated the potential use for such tools in compound selection and prioritization in DMTA cycles and suggest that datasets with known experimental results can be used to assess model performance.