In the past decade, computational approaches in reaction optimization, based on large datasets, have become increasingly common. These datasets are employed to screen for desired properties or to develop machine learning models for rapid predictions. Within the field of bioorthogonal chemistry, Stuyver and Coley recently utilized a large library of (2+3) cycloadditions to screen for biocompatible click reactions. In our study, we developed a straightforward computational workflow that allows for e icient screening of 1,2,4,5-tetrazine reactivity. Using this method, we generated an initial dataset of 1,288 reaction barriers between di erent tetrazines and trans-cyclooctene (TCO) for the initial, rate-limiting Diels-Alder cycloaddition.