An extended VolSurf approach, that additionally includes SHAPE descriptors, was applied to a dataset of 55 quinolones. Bactericidal activity was measured at Bayer AG, Germany, for Gram-negative (Escherichia coli and Pseudomonas aeruginosa) and Gram-positive bacteria (Staphylococcus aureus and Enterococcus faecalis). Chemometric analysis was first approached via a classical VolSurf approach. The following descriptors were found most important: bactericidal activity particularly increases with high values of the best volume (BV11(OH2)) and the minimum energy (Emin1(OH2)) of the water probe, high values of the integy moment (ID(DRY)) of the lipophilic probe, and high values of the hydrophilic region (W(O)) of the hydrogen bond acceptor probe. Best volume (BV31(OH2)) of the water probe and best volume (BV12(DRY)) and lipophilic regions (D(DRY)) of the lipophilic probe as well as H-bonding capacity derived with the CO probe (HB(O)) are inversely related to activity. PLS analysis yields a five-component model with an r(2) of 0.83 and a q(2) of 0.43 after variable selection via fractional factorial design (FFD). Chemometric modeling could be improved by including newly derived SHAPE descriptors, which were merged with the VolSurf descriptors and subjected to PLS analysis. The global model of this extended VolSurf approach is optimal with two components and exhibits a significantly improved statistical quality; a marginally reduced r(2) (0.75 versus 0.83) is more than compensated by a highly improved predictivity with a q(2) of 0.63 versus 0.43. To prove model quality, external prediction of seven test set quinolones was performed. The precise prediction of all test set molecules nicely demonstrates the robustness and statistical significance of the obtained chemometric model using the extended VolSurf approach.