Can we apply out-of-the box feature transfer using pre-trained convolutional neural networks in fine-grained multiclass image categorization tasks? What is the effect of (a) domainspecific fine-tuning and (b) a special-purpose network architecture designed and trained specifically for the target domain? How do these approaches perform in one-class classification? We investigate these questions by tackling two biological object recognition tasks: classification of “cryptic” plants of genus Coprosma and identification of New Zealand moth species. We compare results based on out-of-the-box features extracted using a pre-trained state-of-the-art network to those obtained by finetuning to the target domain, and also evaluate features learned using a simple Siamese network trained only on data from the target domain. For each extracted feature set, we test a number of classifiers, e.g., support vector machines. In addition to multiclass classification, we also consider one-class classification, a scenario that is particularly relevant to biosecurity applications. In the multi-class setting, we find that out-of-the-box lowlevel features extracted from the generic pre-trained network yield high accuracy (90.76%) when coupled with a simple LDA classifier. Fine-tuning improves accuracy only slightly (to 91.6%). Interestingly, features extracted from the much simpler Siamese network trained on data from the target domain lead to comparable results (90.8%). In the one-class classification setting, we note high variability in the area under the ROC curve across feature sets, opening up the possibility of considering an ensemble approach.