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Reverse image search engines have progressed dramatically over the past decade, with no end in sight. Along with the ever-growing amount of indexed material, a number of search giants have enticed their users to sign up for image hosting services, such as Google Photos, giving these search algorithms an endless amount of material for machine learning. On top of this, facial recognition AI is entering the consumer space with products like FindClone and may already be used in some search algorithms, namely with Yandex. There are no publicly available facial recognition programs that use any Western social network, such as Facebook or Instagram, but perhaps it is only a matter of time until something like this emerges, dealing a major blow to online privacy while also (at that great cost) increasing digital research functionality. If you skipped most of the article and are just looking for the bottom line, here are some easy-to-digest tips for reverse image searching: • Use Yandex first, second, and third, and then try Bing and Google if you still can’t find your desired result. • If you are working with source imagery that is not from a Western or former Soviet country, then you may not have much luck. These search engines are hyper-focused on these areas, and struggle for photographs taken in South America, Central America/Caribbean, Africa, and much of Asia. • Increase the resolution of your source image, even if it just means doubling or tripling the resolution until it’s a pixelated mess. None of these search engines can do much with an image that is under 200×200. • Try cropping out elements of the image, or pixelating them if it trips up your results. Most of these search engines will focus on people and their faces like a heat-seeking missile, so pixelate them to focus on the background elements. • If all else fails, get really creative: mirror your image horizontally, add some color filters, or use the clone tool on your image editor to fill in elements on your image that are disrupting searches.