These default pipelines are designed to cover most standard use cases. For instance the default filters pipeline is suitable for preprocessing experimental data and the library cleaning pipeline is suitable for preprocessing of library spectra before library matching or machine learning training. However, we are aware that specific use cases might need additional filtering or different parameterization. Therefore, it is possible to create your own pipeline using existing matchms filters, or to add custom-made filters. Since a filter is defined as a function receiving a spectrum and returning a spectrum, users can add arbitrary functions as custom filters as long as they fulfil this condition. To make it easy to create these new pipelines, the pipeline automatically arranges the filters in a predefined global order. This is critical since many filters depend on other processing steps to be effective. For instance, a filter using information from the adduct field should be run after a filter function retrieving the adduct from the compound name field.