A method comprising using a processor to: obtain a hash vector representing a search subject depicted in an image, the hash vector comprising one or more hashes as a respective one or more components of the hash vector; determine which one or more of the hashes satisfy a threshold criterion; determine which one or more of the components of the hash vector qualify as a scoring component, wherein the one or more components that qualify as a scoring component correspond to a respective one or more hashes that satisfy the threshold criterion and that are represented in a scoring database that is generated based on different examples of a search target; and determine a score representing a similarity of the search subject to the different examples of the search target used to generate the scoring database, wherein the processor determines the score based on the one or more components of the hash vector that qualify as a scoring component, wherein the scoring database comprises hash-weight pairs, each of the hash-weight pairs relating one of the components of the hash vector that qualifies as a scoring component to a scoring weight, wherein using the processor to determine the score comprises using the processor to determine a sum of the scoring weights, wherein obtaining the hash vector comprises using the processor to generate the hash vector by multiplying a feature vector representing the image by a hashing matrix having entries that are random values, wherein generating the scoring database comprises: obtaining a hash vector training set that comprises training hash vectors representing respective examples of the search target common to training images, each of the training hash vectors comprising further one or more hashes as a respective one or more components of the training hash vector; determining which of the further one or more hashes satisfy the threshold criterion; and generating the scoring database based on the one or more hashes that satisfy the threshold criterion; wherein obtaining each of the training hash vectors comprises multiplying each of multiple features vectors representing respective ones of the different examples of the search target by the hashing matrix. obtain a hash vector representing a search subject depicted in an image, the hash vector comprising one or more hashes as a respective one or more components of the hash vector; determine which one or more of the hashes satisfy a threshold criterion; determine which one or more of the components of the hash vector qualify as a scoring component, wherein the one or more components that qualify as a scoring component correspond to a respective one or more hashes that satisfy the threshold criterion and that are represented in a scoring database that is generated based on different examples of a search target; and determine a score representing a similarity of the search subject to the different examples of the search target used to generate the scoring database, wherein the processor determines the score based on the one or more components of the hash vector that qualify as a scoring component, wherein the scoring database comprises hash-weight pairs, each of the hash-weight pairs relating one of the components of the hash vector that qualifies as a scoring component to a scoring weight, wherein using the processor to determine the score comprises using the processor to determine a sum of the scoring weights, wherein obtaining the hash vector comprises using the processor to generate the hash vector by multiplying a feature vector representing the image by a hashing matrix having entries that are random values, wherein generating the scoring database comprises: obtaining a hash vector training set that comprises training hash vectors representing respective examples of the search target common to training images, each of the training hash vectors comprising further one or more hashes as a respective one or more components of the training hash vector; determining which of the further one or more hashes satisfy the threshold criterion; and generating the scoring database based on the one or more hashes that satisfy the threshold criterion; wherein obtaining each of the training hash vectors comprises multiplying each of multiple features vectors representing respective ones of the different examples of the search target by the hashing matrix.