  Modern sky surveys are producing ever larger amounts of observational data,
which makes the application of classical approaches for the classification and
analysis of objects challenging and time-consuming. However, this issue may be
significantly mitigated by the application of automatic machine and deep
learning methods. We propose ULISSE, a new deep learning tool that, starting
from a single prototype object, is capable of identifying objects sharing the
same morphological and photometric properties, and hence of creating a list of
candidate sosia. In this work, we focus on applying our method to the detection
of AGN candidates in a Sloan Digital Sky Survey galaxy sample, since the
identification and classification of Active Galactic Nuclei (AGN) in the
optical band still remains a challenging task in extragalactic astronomy.
Intended for the initial exploration of large sky surveys, ULISSE directly uses
features extracted from the ImageNet dataset to perform a similarity search.
The method is capable of rapidly identifying a list of candidates, starting
from only a single image of a given prototype, without the need for any
time-consuming neural network training. Our experiments show ULISSE is able to
identify AGN candidates based on a combination of host galaxy morphology, color
and the presence of a central nuclear source, with a retrieval efficiency
ranging from 21% to 65% (including composite sources) depending on the
prototype, where the random guess baseline is 12%. We find ULISSE to be most
effective in retrieving AGN in early-type host galaxies, as opposed to
prototypes with spiral- or late-type properties. Based on the results described
in this work, ULISSE can be a promising tool for selecting different types of
astrophysical objects in current and future wide-field surveys (e.g. Euclid,
LSST etc.) that target millions of sources every single night.
