The implementation of computerized algorithms into organic chemistry has had a rich history, with early emphasis centered around deriving linear relationships from observed results. By the 1970s, synthetic chemists had turned their attention to utilizing more complex functions to model more abstract observations. In 1977, Corey et al. published the first recognized retrosynthetic analyzer, LHASA (Logics and Heuristics Applied to Synthetic Analysis) which featured hand coded expert rules resulting in over 30,000 lines of FORTRAN code. With the turn of the century, improvements in computational hardware and the development of new computer learning algorithms, including machine learning (ML), have seeded new avenues for algorithm-based predictive chemistry. ML is the process of taking complex inputs, abstracting their relevant features through non-linear equations, and correlating those features to a given output. Despite its simplistic framework, variations upon this theme have yielded advances in numerous areas including fundamental molecular property prediction (e.g., quantum chemical, ADMET), reaction property prediction (e.g., regioselectivity, yield), and generative modeling. With these more power algorithms come higher data requirements. Where the first data-driven chemistry models may have necessitated a few experimental results, ML often demands tens of thousands of data points. Purely computational datasets from density functional theory (DFT) or semi-empirical methods have been generated and utilized in ML for prediction of quantum chemical properties (e.g., HOMO-LUMO gaps, dipole moments) and in molecular scaffold generation. Benefits of using these datasets include larger sizes and less noise present within each observation. Whilst experimental data is inherently noisier, more expensive, and often more laborious to collate than computational data, it presents a more wholistic representation of a chemical system, even if we do not fully understand the intricacies present within that system. It is therefore important for the community to find ways to incorporate these smaller, experimental datasets as a key feature into ML tools.