Although order of addition may play a role in wet lab chemistry, reaction prediction tasks are often cast as order invariant, where the goal is to predict a set of molecules. However, both of the architectures used for experimental validation of the ORDerly datasets are not agnostic to the ordering of the targets, since the neural networks used predict one molecule at a time in the OHE, and the transformers used predict one token at a time. Incorporating order invariance (and canonicalization) of the molecules into the loss calculation during training may allow for better generalisability of the predictive models, and is an exciting area for further study. It is worth noting that the evaluation metrics used throughout are order invariant.