This paper uses Mutual Information as an alternative variable selection method for quantitative structure-property relationships data. To evaluate the performance of this criterion, the enantioselectivity of 67 molecules, in three different chiral stationary phases, is modelled. Partial Least Squares together with three commonly used variable selection techniques was evaluated and then compared with the results obtained when using Mutual Information together with Support Vector Machines. The results show not only that variable selection is a necessary step in quantitative structure-property relationship modelling, but also that Mutual Information associated with Support Vector Machines is a valuable alternative to Partial Least Squares together with correlation between the explanatory and the response variables or Genetic Algorithms. This study also demonstrates that by producing models that use a rather small set of variables the interpretation can be also be improved.