  We apply a state-of-the-art transformer algorithm to 221 deg$^2$ of the Kilo
Degree Survey (KiDS) to search for new strong gravitational lenses (SGL). We
test four transformer encoders trained on simulated data from the Strong Lens
Finding Challenge on KiDS survey data. The best performing model is fine-tuned
on real images of SGL candidates identified in previous searches. To expand the
dataset for fine-tuning, data augmentation techniques are employed, including
rotation, flipping, transposition, and white noise injection. The network
fine-tuned with rotated, flipped, and transposed images exhibited the best
performance and is used to hunt for SGL in the overlapping region of the Galaxy
And Mass Assembly (GAMA) and KiDS surveys on galaxies up to $z$=0.8. Candidate
SGLs are matched with those from other surveys and examined using GAMA data to
identify blended spectra resulting from the signal from multiple objects in a
fiber. We observe that fine-tuning the transformer encoder to the KiDS data
reduces the number of false positives by 70%. Additionally, applying the
fine-tuned model to a sample of $\sim$ 5,000,000 galaxies results in a list of
$\sim$ 51,000 SGL candidates. Upon visual inspection, this list is narrowed
down to 231 candidates. Combined with the SGL candidates identified in the
model testing, our final sample includes 264 candidates, with 71
high-confidence SGLs of which 44 are new discoveries. We propose fine-tuning
via real augmented images as a viable approach to mitigating false positives
when transitioning from simulated lenses to real surveys. Additionally, we
provide a list of 121 false positives that exhibit features similar to lensed
objects, which can benefit the training of future machine learning models in
this field.
