Over the past years, Remote and Virtual Labs (RVLs) have gained increased attention for their potential to support technology-enhanced science education by enabling science teachers to improve their day-to-day science teaching. Therefore, many educational institutions and scientific organizations have invested efforts for providing online access to state-of-the-art science experiments via RVLs. Currently, there are existing initiatives for the storage and organization of existing RVLs into web-based repositories towards increasing their findability and enabling science teachers to search and retrieve them for further usage into their lesson plans. Nevertheless, most of these repositories adopt metadata models that store limited information related to the pedagogical context of their lesson plans. As a result, science teachers are not supported in selecting RVLs taking into consideration core elements of their lesson planning. In this paper, we aim to tackle this problem by proposing the ASK4Labs a web-based repository for supporting learning design driven RVLs recommendations. Preliminary evaluation results are also described, which indicate that the proposed recommender system can provide robust identification of appropriate RVLs based on the pedagogical context elements of the intended lesson plans. [The work presented in this paper has been partially funded by the European Commission in the context of the Go-Lab project (Grant Agreement No. 317601) under the Information and Communication Technologies (ICT) theme of the 7th Framework Programme for R&D (FP7). For complete proceedings, see ED557311.]