  Expert opinion plays an important role when selecting promising clusters of
chemical compounds in the drug discovery process. We propose a method to
quantify these qualitative assessments using hierarchical models. However, with
the most commonly available computing resources, the high dimensionality of the
vectors of fixed effects and correlated responses renders maximum likelihood
unfeasible in this scenario. We devise a reliable procedure to tackle this
problem and show, using theoretical arguments and simulations, that the new
methodology compares favorably with maximum likelihood, when the latter option
is available. The approach was motivated by a case study, which we present and
analyze.
