  The Hubble Space Telescope (HST) archives constitute a rich dataset of high
resolution images to mine for strong gravitational lenses. While many HST
programs specifically target strong lenses, they can also be present by
coincidence in other HST observations. We aim to identify non-targeted strong
gravitational lenses in almost two decades of images from the ESA it Hubble
Space Telescope archive (eHST), without any prior selection on the lens
properties. We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen
science project to identify strong lenses, alongside asteroid trails, in
publicly available large field-of-view HST images. We visually inspected 2354
objects tagged by citizen scientists as strong lenses to clean the sample and
identify the genuine lenses. We report the detection of 252 strong
gravitational lens candidates, which were not the primary targets of the HST
observations. 198 of them are new, not previously reported by other studies,
consisting of 45 A grades, 74 B grades and 79 C grades. The majority are
galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3
magnitudes fainter than previous HST searches. This sample of strong lenses
with high resolution HST imaging is ideal to follow-up with spectroscopy, for
lens modelling and scientific analyses. This paper presents an unbiased search
of lenses, which enabled us to find a high variety of lens configurations,
including exotic lenses. We demonstrate the power of crowdsourcing in visually
identifying strong lenses and the benefits of exploring large archival
datasets. This study shows the potential of using crowdsourcing in combination
with artificial intelligence for the detection and validation of strong lenses
in future large-scale surveys such as ESA's future mission Euclid or in JWST
archival images.
