  We present a sample of 16 likely strong gravitational lenses identified in
the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using
Convolutional Neural Networks (CNNs). We train two different CNNs on composite
images produced by superimposing simulated gravitational arcs on real Luminous
Red Galaxies observed in VOICE. Specifically, the first CNN is trained on
single-band images and more easily identifies systems with large Einstein
radii, while the second one, trained on composite RGB images, is more accurate
in retrieving systems with smaller Einstein radii. We apply both networks to
real data from the VOICE survey, taking advantage of the high limiting
magnitude (26.1 in the r-band) and low PSF FWHM (0.8" in the r-band) of this
deep survey. We analyse $\sim21,200$ images with $mag_r<21.5$, identifying 257
lens candidates. To retrieve a high-confidence sample and to assess the
accuracy of our technique, nine of the authors perform a visual inspection.
Roughly 75% of the systems are classified as likely lenses by at least one of
the authors. Finally, we assemble the LIVE sample (Lenses In VoicE) composed by
the 16 systems passing the chosen grading threshold. Three of these candidates
show likely lensing features when observed by the Hubble Space Telescope. This
work represents a further confirmation of the ability of CNNs to inspect large
samples of galaxies searching for gravitational lenses. These algorithms will
be crucial to exploit the full scientific potential of forthcoming surveys with
the Euclid satellite and the Vera Rubin Observatory
