  We present 97 new high-quality strong lensing candidates found in the final
$\sim 350\,\rm deg^2$, that completed the full $\sim 1350\,\rm deg^2$ area of
the Kilo-Degree Survey (KiDS). Together with our previous findings, the final
list of high-quality candidates from KiDS sums up to 268 systems. The new
sample is assembled using a new Convolutional Neural Network (CNN) classifier
applied to $r$-band (best seeing) and $g,~r,~i$ color-composited images
separately. This optimizes the complementarity of the morphology and color
information on the identification of strong lensing candidates. We apply the
new classifiers to a sample of luminous red galaxies (LRGs) and a sample of
bright galaxies (BGs) and select candidates that received a high probability to
be a lens from the CNN ($P_{\rm CNN}$). In particular, setting $P_{\rm
CNN}>0.8$ for the LRGs, the $1$-band CNN predicts 1213 candidates, while the
$3$-band classifier yields 1299 candidates, with only $\sim$30\% overlap. For
the BGs, in order to minimize the false positives, we adopt a more conservative
threshold, $P_{\rm CNN} >0.9$, for both CNN classifiers. This results in 3740
newly selected objects. The candidates from the two samples are visually
inspected by 7 co-authors to finally select 97 "high-quality" lens candidates
which received mean scores larger than 6 (on a scale from 0 to 10). We finally
discuss the effect of the seeing on the accuracy of CNN classification and
possible avenues to increase the efficiency of multi-band classifiers, in
preparation of next-generation surveys from ground and space.
