As pointed out by Bahri et al there are multiple challenges in designing an AutoML system, our MTE avoids these in the following ways. First is defining the search space of the autoML pipeline; as our problems are focused on drug discovery targets, we are able to impose a prior on the hyperparameter search space investigated due to the relative consistency of our target space versus other autoML applications. This also extends to the types of molecular featurisations used within the MTE. Secondly, the cold-start problem is mitigated by the ability of the MTE to evaluate many different model configurations in parallel as well as our median pruning strategy. This prevents under performing configurations from consuming resources better utilised by higher performing models during the autoML screening. This median pruning strategy,combined with the curated hyperparameter space also helps tackle the third challenge of extensive run time of the autoML screening. Fourthly, our MTE is able to scale to large dataset sizes through a combination of curated search space, automatic termination of low performing training configurations and the pre-computation of numerous descriptors within the feature store. The feature store allows for a highly optimised computation of multiple molecular feature groups which are able to be dynamically read throughout the entire autoML process including various cross fold validation experiments.