In this manuscript, we present a very fast and inexpensive approach where ligands of known E3 ligases are described by a simple and effective pharmacophore fingerprinting scheme, known as Extended Reduced Graph (ErG) . Each ErG bit forms the basis for a multiclass classification model where singular E3 ligase target names are used as labels. This is the first example of such a classification approach in the E3 ligase field. The resultant statistical model has an accuracy of 93.8% and thus is able to assign the correct E3 ligase binder to previously known E3 ligase. As a result of this, such an approach allows us to predict the probability of each molecule to bind to different E3 ligases. We validated this model on commercial libraries for the rational design of E3 ligase binders.