  Data-hungry machine learning methods have become a new standard to
efficiently navigate chemical compound space for molecular and materials design
and discovery. Due to the severe scarcity and cost of high-quality experimental
or synthetic simulated training data, however, data-acquisition costs can be
considerable. Relying on reasonably accurate approximate baseline labels with
low computational complexity represents one of the most effective strategies to
curb data-needs, e.g.~through $\Delta$-, transfer-, or multi-fidelity learning.
We introduce baseline labels in the form of a generic coarse-graining Hammett
Inspired Product (HIP) {\em Ansatz} which generalizes the empirical Hammett
equation towards arbitrary systems and properties. Numerical evidence for the
applicability of HIP includes solvation free energies of molecules, formation
energies of quaternary elpasolite crystals, carbon adsorption energies on
heterogeneous catalytic surfaces, HOMO-LUMO gaps of metallorganic complexes,
and activation energies for S$_\text{N}$2 reactions. After calibration on the
same training sets, HIP yields an effective baseline for improved
$\Delta$-quantum machine learning models with superior data-efficiency when
compared to previously introduced specialised domain-specific models.
