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We present a sample of 16 likely strong gravitational lenses identified inthe VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) usingConvolutional Neural Networks (CNNs). We train two different CNNs on compositeimages produced by superimposing simulated gravitational arcs on real LuminousRed Galaxies observed in VOICE. Specifically, the first CNN is trained onsingle-band images and more easily identifies systems with large Einsteinradii, while the second one, trained on composite RGB images, is more accuratein retrieving systems with smaller Einstein radii. We apply both networks toreal data from the VOICE survey, taking advantage of the high limitingmagnitude (26.1 in the r-band) and low PSF FWHM (0.8" in the r-band) of thisdeep survey. We analyse $\sim21,200$ images with $mag_r<21.5$, identifying 257lens candidates. To retrieve a high-confidence sample and to assess theaccuracy 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 ofthe authors. Finally, we assemble the LIVE sample (Lenses In VoicE) composed bythe 16 systems passing the chosen grading threshold. Three of these candidatesshow likely lensing features when observed by the Hubble Space Telescope. Thiswork represents a further confirmation of the ability of CNNs to inspect largesamples of galaxies searching for gravitational lenses. These algorithms willbe crucial to exploit the full scientific potential of forthcoming surveys withthe Euclid satellite and the Vera Rubin Observatory

Lenses In VoicE (LIVE): Searching for strong gravitational lenses in the VOICE@VST survey using Convolutional Neural Networks