A number of recent approaches are based on statistical evaluation of existing large bodies of reaction data by unleashing modern machine learning methods on these data sets. Molecular structure representation is often done by SMILES. While impressive results have been achieved by these approaches whose central machine-learning algorithms may or may not be aware of chemistry at all, we see several advantages of SAVI compared to these approaches. Learning from existing datasets will always learn what is known, and preferentially learn what is widely used, i.e. strongly represented in the learning set. CHMTRN/PATRAN transforms can, in contrast, be used to add brand-new or unpublished chemistry into SAVI without having to wait for reaction databases to fill up with examples of such reactions. This has not been used much for SAVI up until now because we first wanted to populate the SAVI transform set with reliable, well-known chemistry that would be readily accepted by chemists. However, we have added new transforms in the recent past (not used for creation of the data presented here) as new synthetic approaches are being published. The latest examples include sulfonimidamide synthesis and modular click chemistry. With accelerating advances in synthetic organic chemistry we expect rapid growth of SAVI. The usage of sophisticated transforms that incorporate a scoring system makes it possible to use negative outcomes of the reaction logic (KILLed reactions, reactions with SUBTRACT demerits) to create large sets of (computationally) failed reactions, which may be useful for, e.g., machine learning approaches. Such efforts are currently being investigated.