Machine learning (ML) has emerged as an area of increasing interest in the field of chemistry, offering predictive insights in areas such as retrosynthesis, catalysis 16 and drug design. In a simplistic sense, ML involves mathematical algorithms learning patterns and relationships from the input data during a 'training' process. Once trained, the model can make predictions for new, unlabelled data -in a chemical context, this might involve predicting the energy of a structure not included in the training set. ML has been shown to significantly accelerate computational processes that rely on traditional quantum methods -in the context of understanding chemical reactivity, considerable attention has been given to activation barriers, with ML enabling the prediction of barriers at DFT-level accuracy or above in seconds, compared to hours required by QM methods. However, the application of ML for accessing TS geometries is still in its infancy, with the first paper published to tackle this problem dating back to just 2020. Since then, there has been significant interest in this area of research with a number of papers published that explore alternative approaches and datasets, with varying degrees of success.