Figures show heat maps for a molecule (3) with both a low error and low FPA overlap (-0.33). In this case the ML-attributions are all negative, while the predicted logP value is positive. Comparison of the ML attribution vector and FPA vector shows that the problem lies mainly with the C atoms in the furan ring, which make negative contributions to the logP. Removing the N proton changes the sign of these contributions and increases the overlap to 0.73 (Figure ), so the "sign problem" in the furan ring is related to the protonation state of the neighboring pyrazole ring. We investigated several possible explanations, such as bit collisions between fragments in these two rings or the effect of FP fragments that connect the two rings, but the reason turns out to be a bit more complicated. The changes in predicted logP that gives rise to the heat map has three main contributions (Figure ): bit 1171 (the NH 2 group), 1386 (the NH + group), and 1750 (the furan C atoms). In fact a FP vector with only these three bits results in a predicted logP value (0.91) that is very similar to the value predicted with all 45 on-bits (1.07). Removing bit 1750 from this FP decreases the predicted logP by 0.36 (i.e. bit 1750 makes a positive logP contribution), while removing bit 1750 from the full FP vector increases logP by 0.35, which gives rise to the negative contours on the furan ring. Clearly, at least one additional bit is needed for the negative furan ring contributions and that bit turns out to be 875, which represents the three C atoms of the pyrazole ring (Figure ). Note that all three bits (875, 1171, and 1386) must be on for bit 1750 to make a negative contribution, which is why deprotonation (which removes bit 1386) changes the sign of bit 1750's contribution. This is a case of overfitting in the sense that the ML model has learned a non-additive rule for an additive property, but we will qualify this point further at the end of this subsection.