In this paper, a modified Clonal Selection Algorithm (CSA) is proposed to learn qualitative compartmental models. Dif- ferent from traditional AI search algorithm, this population- based approach employs antibody repertoire to perform ran- dom search, which is suitable for the ragged and multi-modal landscape of qualitative model space. Experimental result shows that this algorithm can obtain the same kernel sets and learning reliability as previous work for learning the two- compartment model, and it can also search out the target model when learning the more complex three-compartment model. Although this algorithm does not succeed in learn- ing the four-compartment model, promising result is still obtained.