  Derivatives of BODIPY are popular fluorophores due to their synthetic
feasibility, structural rigidity, high quantum yield, and tunable spectroscopic
properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV,
combinations of functional groups and substitution sites can shift the peak
position by +/- 1 eV. Time-dependent long-range corrected hybrid density
functional methods can model the lowest excitation energies offering a
semi-quantitative precision of +/- 0.3 eV. Alas, the chemical space of BODIPYs
stemming from combinatorial introduction of -- even a few dozen -- substituents
is too large for brute-force high-throughput modeling. To navigate this vast
space, we select 77,412 molecules and train a kernel-based quantum machine
learning model providing < 2% hold-out error. Further reuse of the results
presented here to navigate the entire BODIPY universe comprising over 253 giga
(253 x 10^9) molecules is demonstrated by inverse-designing candidates with
desired target excitation energies.
