  We investigate the ability of human 'expert' classifiers to identify strong
gravitational lens candidates in Dark Energy Survey like imaging. We recruited
a total of 55 people that completed more than 25$\%$ of the project. During the
classification task, we present to the participants 1489 images. The sample
contains a variety of data including lens simulations, real lenses, non-lens
examples, and unlabeled data. We find that experts are extremely good at
finding bright, well-resolved Einstein rings, whilst arcs with $g$-band
signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times
the seeing are rarely recovered. Very few non-lenses are scored highly. There
is substantial variation in the performance of individual classifiers, but they
do not appear to depend on the classifier's experience, confidence or academic
position. These variations can be mitigated with a team of 6 or more
independent classifiers. Our results give confidence that humans are a reliable
pruning step for lens candidates, providing pure and quantifiably complete
samples for follow-up studies.
