Naturalistic surveillance tasks provide a rich source of eye-tracking data. It can be challenging to make meaningful comparisons using standard eye-tracking analysis techniques such as saccade frequency or blink rate in surveillance studies due to the temporal irregularity of events of interest. Naturalistic research environments present unique challenges, such as requiring specialized or expert analysts, small sample size, and long data collection sessions. These constraints demand rich data and sophisticated analyses, particularly in prescriptive naturalistic environments where problems must be thoroughly understood to implement effective and practical solutions. Using a small sample of expert surveillance analysts and an equal-sized sample of novices, we computed scanpath similarity on a variety of surveillance data using the ScanMatch Matlab tool. ScanMatch implements an algorithm initially developed for DNA protein sequence comparisons and provides a similarity score for two scanpaths based on their morphology and, optionally, duration in an area of interest. Both experts and novices showed equal dwell time on targets regardless of identification accuracy and both samples showed higher scanpath consistency across participants as a function of target type rather than individual subjects showing a particular scanpath preference. Our results show that scanpath analysis can be leveraged as a highly effective computer-based methodology to characterize surveillance identification errors and guide the implementation of solutions. Similarity scores can also provide insight into processes guiding visual search.