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One of the current routes in developing antiasthmatics is CysLT(1) receptor antagonism. For a training set of 54 CysLT(1) receptor antagonists of the quinolinyl(bridged)aryl type we developed chemometric QSAR models applying GRID independent descriptors (=GRIND). PLS analysis resulted in a two-component model explaining 67% of the variance for CysLT(1) receptor binding (r2=0.67, SDEC = 0.47, q2=0.54). GRIND variables 11-50 and 22-55 are responsible for high-affinity binding; variable 11-62 is detrimental. The predictivity of the above chemometric model is tested with a set of 69 CysLT(1) receptor antagonists, exhibiting varying chemical similarity to the training set. Nearly 50% of the test set are quite well predicted. The quality of prediction coincides in part with chemical subclassification: phenylene bridged compounds are quite well predicted; for structures with bridging heterocycles predictions are rather poor. For explaining the outlier behavior, a PLS discriminant analysis including the training set and the strongest outliers of the test set was performed. The scores plot of discriminant PLS shows an almost complete separation between the two subsets. A PLS coefficients plot explains which GRIND variables are important for the discrimination between the training set and the outliers of the test set.