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We report new high-quality galaxy scale strong lens candidates found in theKilo Degree Survey data release 4 using Machine Learning. We have developed anew Convolutional Neural Network (CNN) classifier to search for gravitationalarcs, following the prescription by \cite{2019MNRAS.484.3879P} and using only$r-$band images. We have applied the CNN to two "predictive samples": aLuminous red galaxy (LRG) and a "bright galaxy" (BG) sample ($r<21$). We havefound 286 new high probability candidates, 133 from the LRG sample and 153 fromthe BG sample. We have then ranked these candidates based on a value thatcombines the CNN likelihood to be a lens and the human score resulting fromvisual inspection (P-value) and we present here the highest 82 rankedcandidates with P-values $\ge 0.5$. All these high-quality candidates haveobvious arc or point-like features around the central red defector. Moreover,we define the best 26 objects, all with scores P-values $\ge 0.7$ as a "goldensample" of candidates. This sample is expected to contain very few falsepositives and thus it is suitable for follow-up observations. The new lenscandidates come partially from the the more extended footprint adopted herewith respect to the previous analyses, partially from a larger predictivesample (also including the BG sample). These results show that machine learningtools are very promising to find strong lenses in large surveys and morecandidates that can be found by enlarging the predictive samples beyond thestandard assumption of LRGs. In the future, we plan to apply our CNN to thedata from next-generation surveys such as the Large Synoptic Survey Telescope,Euclid, and the Chinese Space Station Optical Survey.

New high-quality strong lens candidates with deep learning in the Kilo Degree Survey