Despite the increasing use of high-throughput experiments in molecular biology, methods for evaluating and classifying the acquired results has not kept pace, requiring significant manual efforts to do so. Here we present CiRCus, a framework to generate custom machine learning models to classify results from high-throughput proteomics binding experiments. We show the experimental procedure that guided us to the layout of this framework as well as the usage of the framework on an example dataset consisting of 557,166 protein:drug binding curves achieving an AUC of 0.9987. By applying our classifier to the data, only 6% of the data might require manual investigation. CiRCus bundles two applications, a minimal interface to label a training dataset (CindeR) and an interface for the generation of a random forest classifiers with optional optimization of pre-trained models (CurveClassification). CiRCus is available on github.com/kusterlab accompanied by an in-depth user manual and video tutorial.