Neuro-symbolic AI is a growing field of research aiming to combine neural networks learning capabilities with the reasoning abilities of symbolic systems. This hybridization can take many shapes. In this paper, we propose an approach that leverages logical background knowledge to improve a neural-based system solving a task of structured multi-label classification. In the literature, two main neuro-symbolic approaches have been proposed for this integration : semantic conditioning and semantic regularization. We introduce a third neuro-symbolic technique called semantic conditioning at inference (SCI), a modification of semantic conditioning which only constrains the system during inference. We also develop a methodology to quantitatively estimate the overall improvements brought by SCI and apply it on several vision datasets. The results indicate that SCI can be used to improve the parameters and data efficiency of neural-based systems while increasing their asymptotic accuracy.