Purpose: Liposomes have been proposed to be a means of selectively targeting cancer sites for diagnostic and therapeutic applications. The focus of this work was the evaluation of radiolabeled PEGylated liposomes derivatized with varying amounts of a cyclic arginyl-glycyl-aspartic acid (RGD) peptide. RGD peptides are known to bind to α v β 3 integrin receptors overexpressed during tumor-induced angiogenesis. Methods: Several liposomal nanoparticles carrying the RGD peptide targeting sequence (RLPs) were synthesized using a combination of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine, cholesterol, diethylenetriaminepentaacetic acid-derivatized lipids for radiolabeling, a polyethylene glycol (PEG) building block, and a lipid-based RGD building block. Relative amounts of RGD and PEG building blocks were varied. In vitro binding affinities were determined using isolated α v β 3 integrin receptors incubated with different concentrations of RLPs in competition with iodine-125-labeled cyclo-(-RGDyV-). Binding of the indium-111-labeled RLPs was also evaluated. Biodistribution and micro single photon emission computed tomography/computed tomography imaging studies were performed in nude mice using different tumor xenograft models. Results: RLPs were labeled with indium-111 with high radiochemical yields. In vitro binding studies of RLPs with different RGD/PEG loading revealed good binding to isolated receptors, which was dependent on the extent of RGD and PEG loading. Binding increased with higher RGD loading, whereas reduced binding was found with higher PEG loading. Biodistribution showed increased circulating time for PEGylated RLPs, but no dependence on RGD loading. Both biodistribution and micro single photon emission computed tomography/computed tomography imaging studies revealed low, nonspecific tumor uptake values. Conclusion: In this study, RLPs for targeting angiogenesis were described. Even though good binding to α v β 3 integrin receptors was found in vitro, the balance between PEGylation and RGD loading clearly requires optimization to achieve targeting in vivo. These data form the basis for future development and provide a platform for the investigation of multimodal approaches.