Life’s origin and chemical evolution requires continuous and substantial selective processes at the molecular scale. However, the spontaneous emergence of selection, its mechanism and system-level influence are still insufficiently explored. To address this, an automated experimental framework has been devised to identify selection in a recursive system of oligomerizing molecules with closed-loop analytics. The approach is based on Assembly Theory, using Molecular Assembly (MA) index as an inherent complexity measure of molecules and molecular networks. A string-based MA model was developed to assist in the efficient analysis of diverse lengthy oligomers and to allow string information procedures. Coupled with smart algorithmic decision-making, the system will attempt to maximize the molecular network’s complexity in the reactor over recursive cycles. Following patterns of increasing chemical complexity in the molecular system could reveal definite traces of selection and determine the conditions and agents that promote it. This work elucidates why improbable complex states emerge, pertinent to life’s origin and its major evolutionary transitions.