Identifying change in remote sensing observations - such as satellite images - is an important step for many applications. In practice, the aim is usually not to find all differences, but rather only specific types of change, such as urban development, deforestation, or even more specialized categories like road construction. However, often there are no large public data sets available for very fine-grained tasks or non-standard usecases that might, for example, occur in developing countries, and to collect the amount of training data needed for most supervised learning methods is very costly and often prohibitive.For this reason, we formulate the problem of few-shot filtering, where we are provided with a relatively large change detection data set and a few instances of one particular type of change that we try to “filter out” of the learned changes. This would enable stakeholders to work with the available resources and adapt them to their individual needs, for a wide range of applications.