This paper presents work in progress from the INFUSIS project and contains initial experimentation, using publicly available medicinal chemistry datasets, on obtaining comprehensible QSAR models. Three techniques are evaluated on both predictive performance, measured as accuracy, and comprehensibility, measured as model size. The chosen techniques are J48 decision trees and JRip and Chipper decision lists. The results show that J48 obtains superior accuracy and that Chipper performs best of the two decision list algorithms on accuracy. Furthermore, it is seen that, regarding accuracy, all techniques benefit from feature reduction, which almost always results in increased accuracy. Regarding comprehensibility, JRip obtains the smallest models, followed by Chipper, with J48 producing the largest models. For model size, feature reduction is not seen to be universally beneficial; only J48 produces smaller models for the reduced datasets, while both decision list algorithms actually produce larger models on average. The overall conclusion is that, for these datasets, there exists a definite tradeoff between accuracy and comprehensibility that needs to be investigated further.