Group-testing is an important element of biosecurity operations, designed to reduce the risk of introducing exotic pests and pathogens with imported agricultural products. Groups of units, such as seeds, are selected from a consignment, and tested for contamination, with a positive or negative test returned for each group. These schemes are usually designed such that the probability of detecting contamination is high assuming random mixing and a somewhat arbitrary design prevalence. We propose supplementing this approach with an assessment of the distribution of the number of contaminated units conditional on testing results. We develop beta-binomial models allowing for between-consignment variability in contamination levels, with a further layer of nesting to allow for possible clustering within the groups for testing. The latent beta distributions can be considered as priors and chosen based on expert judgement, or estimated from historical test results. We show that the parameter controlling within-group clustering is, unsurprisingly, effectively non-identifiable. It can be handled by sensitivity analysis, but we demonstrate theoretically and empirically that the probability of a consignment with contamination evading detection is almost perfectly robust to mis-specification of this clustering. We apply the new models to large cucurbit seed lots imported into Australia where they provide important new insights for biosecurity regulation.