To create such a methodology, a High Throughput Experimentation Analyzer (HiTEA) was developed which can deduce the reactome of any HTE dataset. Whilst common chemistry datasets such as the CAS, Reaxys, 13 USPTO, Pistachio, or the Open Reaction Database have impressive coverage, it was a concern that the high level of overlap between their reactions and literature data would shape these datasets' reactomes to be indistinguishable to literature reactomes . This would make it difficult to explore the discrepancies between the data and the literature reactomes, something that is likely to be possible utilizing HTE datasets and a fundamental feature of HiTEA that we wished to investigate. Thus, a HiTEA analysis was performed on a ground-breaking release of 10 years of historical medicinal chemistry HTE data. It is an unprecedently large dataset, acquired over 10+ years and spans a wide range of reaction classes (Fig. ). Within it are over 39,000 reactions conditions spanning over 350 target products. The reactions are split across numerous classes, ranging from thousands of reactions to tens of reactions, whose reactants and reagents may be over-represented or under-represented. These challenges highlight the necessity of statistical analyses, which can understand the data even in these skewed environments. HiTEA's analysis of several classic reaction types reveal some notable biases as well as some unexpected findings which warrant further investigation.