  The volume of data that will be produced by new-generation surveys requires
automatic classification methods to select and analyze sources. Indeed, this is
the case for the search for strong gravitational lenses, where the population
of the detectable lensed sources is only a very small fraction of the full
source population. We apply for the first time a morphological classification
method based on a Convolutional Neural Network (CNN) for recognizing strong
gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS),
one of the current-generation optical wide surveys. The CNN is currently
optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about
twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude
selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN
retrieves 761 strong-lens candidates and correctly classifies two out of three
of the known lenses. The misclassified lens has an Einstein radius below the
range on which the algorithm is trained. We down-select the most reliable 56
candidates by a joint visual inspection. This final sample is presented and
discussed. A conservative estimate based on our results shows that with our
proposed method it should be possible to find $\sim100$ massive LRG-galaxy
lenses at $z\lsim 0.4$ in KiDS when completed. In the most optimistic scenario
this number can grow considerably (to maximally $\sim$2400 lenses), when
widening the colour-magnitude selection and training the CNN to recognize
smaller image-separation lens systems.
