The presence of reactions with low yields within a dataset is expected to be key for accurate predictions . Very recently, Glorius and co-workers demonstrated that data expansion strategy with artificial low yields could boost ML predictive performances on proprietary databases . However, this could introduce biases that have not yet been studied. HTE data display relatively homogeneous yield distribution, with many negative examples. Meanwhile, searching the proprietary database Sci-Finder n (Fig. ) for reactions matching the NiCOlit chemical reaction space returns 2,203 reactions with a clear bias toward high yields : 60% of them have a yield above 70%. NiCOlit yield distribution lays between HTE and Sci-Finder n data, with a significant amount of zero yields experiments but few reactions in the 20 to 40% yield range. This suggests a reporting bias in published reaction data, and an even stronger bias in proprietary databases. The fact that published reaction data contains significantly more negative examples than proprietary databases raises hope that such data could prove much more useful for machine learning model building.