We used ML regressors trained on experimental data to generate expanded reaction condition spaces by predicting reaction outcomes for a larger set of experiments that, while not present in the original training data, fall primarily within the domain of the training set. We refer to these ML regressors as emulators. We used emulators from the Olympus benchmarking framework (suzuki_i to suzuki_iv) to generate the Suzuki Coupling (i to iv) virtual benchmark datasets. Similarly, we also trained a Multi-layer Perceptron (MLP) using TensorFlow on experimental data from Torres et al. (EDBO+) to generate the C-H arylation virtual benchmark dataset. A detailed description of all (emulated) virtual benchmark datasets are included in Supplementary Information section 1 and the data accompanying this paper.